A Proposal to NOAO

for the

Dark Energy Survey

July 15, 2004

Submitted by

Fermilab

J. Annis, S. Dodelson, B. Flaugher, J. Frieman, M. Gladders\*, L. Hui,
S. Kent, H. Lin, P. Limon, J. Peoples, V. Scarpine, A. Stebbins, C.
Stoughton, D. Tucker, W. Wester

\* Carnegie Observatories

University of Illinois at Urbana-Champaign

R. Brunner, I. Karliner, J. Mohr, R. Plante, M. Selen, J. Thaler

University of Chicago

J. Carlstrom, S. Dodelson, J. Frieman, W. Hu, S. Kent, E. Sheldon, R.
Wechsler

University of California Berkeley and Lawrence Berkeley National
Laboratory

G. Aldering, N. Roe, C. Bebek, M. Levi, S. Perlmutter

In collaboration with:

Cerro Tololo Inter-American Observatory

T. Abbott, R. C. Smith, N. Suntzeff, A. Walker

Project Director: John Peoples, <peop@fnal.gov>, Fermilab, P.O. Box; MS
127, Batavia, IL 60510

**T A B L E O F C O N T E N T S**

1.  **Introduction and Summary .. 1**

2.  **The Science Program of the Dark Energy Survey .. 5**

3.  **Science Requirements
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    34**

4.  **Survey Planning 48**

5.  **The Survey Instrument
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    80**

6.  **Data Management
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    134**

7.  **Project Management
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    155**

8.  **The Relationship of DES to Other Astrophysics Projects
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9.  **Community Service
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    185**

10. **Conclusions
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    187**

Proposal to NOAO for the Dark Energy Survey

**1. Introduction and Summary**

The National Optical Astronomical Observatory (NOAO) issued an
Announcement of Opportunity (AO) in December 2003 that requested
proposals for the development of a major new instrument for the Blanco 4
meter telescope at Cerro Tololo Inter-American Observatory (CTIO) in
partnership with NOAO. The AO encouraged the construction of an
instrument that could exploit the wide field capability of the prime
focus. We, a group of astrophysicists and physicists from Fermilab, the
University of Illinois, the University of Chicago, the Lawrence Berkeley
National Laboratory (LBNL), and CTIO formed the Dark Energy Survey
Collaboration and are submitting this proposal to build a Survey
Instrument consisting of a 520 megapixel CCD camera and wide field
optical corrector for the prime focus. Our proposed instrument will have
an effective field of view of about 3 square degrees and will have four
filters that will enable observations in the g, r, i, and z bands. These
features will give it greater survey power than any optical-near
infrared camera currently in existence. We plan to use this instrument
to carry out a deep imaging survey in these four bands over an area of
approximately 5000 square degrees over the course of five years. Our
collaboration will also develop the processing and archiving software
for the data produced with our instrument and will provide the
facilities and staff to process, archive, and distribute the processed
data from our survey.

A number of national panel reports, including Connecting Quarks with the
Cosmos (2003), the Physics of the Universe (2004), and the Quantum
Universe (2004), have assigned high priority to answering the question:
What is Dark Energy? Our collaboration proposes to determine the dark
energy equation of state parameter, *w***,** using four complementary
techniques in a single survey: (i) the redshift distribution and
clustering evolution of galaxy clusters, (ii) weak gravitational lensing
on large scales, (iii) the evolution of galaxy clustering, and (iv) type
Ia supernova distances. We selected 4000 square degrees of our proposed
survey area to coincide with the area that the South Pole Telescope
(SPT) collaboration plans to observe. The SPT project, which is already
under construction, will detect and measure masses of galaxy clusters
through the Sunyaev-Zeldovich effect. The DES will provide photometric
redshifts for these clusters; in combination, the two projects will
determine the redshift distribution and clustering evolution of the
galaxy clusters. The two collaborations plan to carry out a joint
analysis of the clusters in the overlap area. We expect that these four
independent measurements of *w* when taken together will provide a
measurement with a statistical precision of order 5%, substantially
better than the present combined precision of \~15%. Moreover, our use
of multiple, complementary techniques allow us to probe for possible
systematic errors and provide a far more robust constraint than any
single technique can offer.

The Dark Energy Survey will catalog and provide photometric redshifts
for roughly 300 million galaxies out to a redshift of order 1 (and a
smaller number beyond that). Thus the DES will be substantially deeper
and cover a larger volume than the Sloan Digital Sky Survey (SDSS): the
DES galaxy catalog will contain nearly four times the number of galaxies
contained in the SDSS catalog, the largest CCD imaging survey to date.
Like the SDSS, which has already had a deep impact on science, we expect
that the public archive from our survey will yield rich scientific data
and discoveries in a very wide range of topics of interest to
astrophysicists, cosmologists, and particle physicists.

Fermilab will lead the effort to build the Survey Instrument, and it
will take the responsibility for designing and building the camera, the
prime focus cage, and the mechanical and electrical interfaces to the
Blanco telescope. The University of Chicago plans to take the leadership
role in designing, acquiring, and testing the optics, with assistance
from Fermilab and CTIO. LBNL will manage the acquisition of the CCDs and
deliver them to Fermilab. Fermilab will package, test, and grade the
CCDs and then install them on the focal plane at its Silicon Detector
Facility. The University of Illinois will design and build the data
acquisition system, including the interfaces to the electronics in the
prime focus cage. They will also provide all of the necessary software
to operate the system in test environments at Fermilab and the operating
environment at Cerro Tololo. CTIO will fabricate some of the mechanical
components in its shops and provide the collaboration with the benefit
of its experience in building and operating Mosaic II and the Blanco.
CTIO will take the lead in defining the communications, electrical
power, and cooling interfaces with the Blanco. The Survey Instrument
team will integrate the camera, the data acquisition system, and the
related mechanical and electrical components into a fully integrated,
tested, and functioning unit at Fermilab prior to shipping the
instrument to Cerro Tololo. CTIO will lead the instrument commissioning
on the Blanco, with support from all of the participating institutions.

The University of Illinois Astronomy Department will lead the data
management and archiving effort. Their efforts will be supported by the
National Center for Supercomputing Applications (NCSA), the Fermilab
Experimental Astrophysics Group, and the NOAO Data Products Program.
This will allow the Collaboration to take advantage of the experience
that NCSA and Fermilab have gained in processing, archiving, and
distributing astronomical data. The University of Illinois will also
distribute the Survey data and catalogs to the collaboration; after a
period for validation of the archive, it will release the archive to the
public in partnership with NOAO.

The Collaboration began work on the Reference Designs for the Survey
Instrument and the Archive in November 2003. In March 2004 it submitted
a proposal based on the Reference Designs to Fermilab, requesting
support for the design and development of the Survey Instrument. The
proposal was considered at the April meeting of the Fermilab Physics
Advisory Committee (PAC) and, on the basis of the positive PAC
recommendation, the Fermilab Director authorized the preparation of a
more complete design and cost estimate. The Collaboration submitted the
revised proposal, containing more detailed Reference Designs,
preliminary cost estimates, and a funding model, to Fermilab on June 7.
A special committee, appointed by the Director, reviewed the revised
proposal, including the funding model, on June 7 and 8. The PAC
considered the DES project for the second time during the week of June
21 and recommended stage 1 approval, which has since been given by the
Director.

The estimated cost of the Survey Instrument is \$ 18.40 M. This estimate
includes \$11.45 M for the cost of materials and services (M&S) and
\$6.95 M for the engineering and technical labor costs that will be
incurred by Fermilab and the University of Illinois. Since the
Collaboration has asked the participating institutions to make the cost
of the overhead a contribution to the Survey Instrument, these figures
do not include institutional overhead. Fermilab and the University of
Illinois plan to contribute the scientific and technical labor for the
Instrument. These cost figures have been escalated for inflation and are
in as spent dollars.

The estimated cost of Data Management is \$1.8M, primarily for technical
labor and computing professionals and a small amount for equipment. The
University of Illinois administration and NCSA have already made a
commitment to provide a significant fraction (\$0.6M) of the effort
required for data processing and archiving as a contribution to the
project. NCSA has also agreed to provide the computing and archiving
equipment needed during the software development phase and later for
base operations as well as the operations staff to operate it as a
contribution to the project. An additional \$1.2 M is needed to cover
the cost of the technical staff at U Illinois and NCSA for software
development and hardware. The Collaboration plans to obtain the funds
for these costs from the NSF.

On the basis of the current requests and commitments, the Collaboration
needs to obtain an additional \~\$5 M of cash funds to fully fund the
construction and commissioning phases of the Survey. The Collaboration
plans to approach the NSF, the participating universities, and private
foundations for the remaining funds. Faculty members from the University
of Chicago and the University of Illinois have requested funds from
their institutions to help pay the M&S costs for the Survey Instrument
development phase that will begin this September 2004. At U. Illinois,
the NCSA director and the Dean of the LAS college are enthusiastic about
the Dark Energy Survey and have responded with pledges of institutional
support. At U. Chicago, the Kavli Institute for Cosmological Physics has
also responded favorably and will help support the project. Finally the
Collaboration will seek additional partners that can contribute to the
project in ways that will reduce the amount of funds that they plan to
request from the DOE and NSF.

If funding can be obtained in accordance with the funding model, the
Survey Instrument construction phase will begin in October 2005 and it
will be completed at the end of 2008. After integration and acceptance
testing at Fermilab, it will be shipped to CTIO in early 2009. In
parallel with the instrument construction and commissioning, the data
processing pipelines will be built, tested, and deployed with the Survey
Instrument. Following a commissioning period of several months, the
Survey Instrument at CTIO and the pipelines and computing systems at
NCSA will be ready for survey operations. The first observations of the
survey region near the south galactic cap could begin in September 2009.
The survey will continue through five observing seasons
(September-February), 2009-2013. Initial Dark Energy constraints could
be completed in 2011, after the first two observing seasons, weather
permitting. During the year prior to October 2005, the Collaboration
will focus its efforts on the completion of the technology development
of the CCDs, the creation of a CCD test facility at Fermilab, and the
final design of the optical corrector, because the CCDs and the optical
corrector present the greatest cost and schedule risks to the project.

We request that NOAO award the DES collaboration 30% of the observing
time on the Blanco telescope over the five-year period beginning
September 2009, in exchange for providing the new instrument and the
software systems. Moreover, we request that CTIO upgrade the Blanco
telescope controls and infrastructure to efficiently accommodate the new
instrument prior to delivery of the instrument and software, as noted in
the AO. In the event that this request is granted, the Collaboration
will submit proposals to the Department of Energy Office of Science and
the Astronomy Division of the National Science Foundation for funding.

The remainder of this proposal is organized in chapters as follows:
Chapter 2 presents the compelling science that we expect to obtain with
the DES, Chapter 3 contains the DES scientific requirements that flow
from our science goals, and Chapter 4 describes the preliminary strategy
that we propose for observations. The Reference Design of the Survey
Instrument and the initial formulation of our Data Management Plan are
contained in Chapters 5 and 6. Chapter 7 contains the project management
plan and the relevant prior experience in instrumentation and large
surveys. Chapter 8 describes the relationship of the Dark Energy Survey
to other relevant astrophysical surveys in which the Dark Energy
Collaboration members are engaged and the temporal relationship of our
survey to other closely related astrophysics projects. Chapter 9
provides an outline of the science that could be done by general
observers with the proposed instrument. Finally, Chapter 10 summarizes
the observing time that the collaboration is requesting.

**2. The Science Program of the Dark Energy Survey**

What is the nature of the Dark Energy? To address this question, the
dark energy must be probed by multiple, complementary methods with
independent systematic errors and different cosmological parameter
degeneracies. The Dark Energy Survey is designed to pursue several of
the most promising of these methods in the context of a single
experiment and thereby achieve a substantial advance in dark energy
precision. In this chapter, we describe the science goals and drivers of
the Dark Energy Survey. In the following chapters, these goals are
translated into science requirements, hardware design, and survey
strategy.

The Dark Energy Survey is designed to provide data that will yield
accurate multi-band fluxes and shapes of galaxies to i = 24 over an area
of 5000 square degrees (see chapters 3 and 4). The fluxes, and the
resulting colors, will yield galaxy photometric redshift estimates
(described in Section 2.7), a linchpin of several dark energy probes.
The survey area is chosen to encompass the Sunyaev-Zel'dovich effect
(SZE) cluster survey that will be carried out with the South Pole
Telescope (SPT); the DES will obtain photometric redshifts for the vast
majority of clusters detected by the SPT (all those out to z=1.3).
Together, the SPT and DES data will enable measurement of the cluster
redshift distribution and the cluster power spectrum, both powerful dark
energy probes. In particular, the DES flux limit requirements are chosen
so that we can optically identify and measure accurate photometric
redshifts of *all* clusters and even some groups in the survey area to a
redshift *z* \~ 1.0 and of a fraction of the clusters extending to *z*
\~ 1.5. The DES will also measure the shapes of distant galaxies to
infer the shear caused by weak gravitational lensing. The weak lensing
measurements will independently calibrate the masses for massive
clusters out to redshift *z* \~ 0.7, enabling a purely optical cluster
abundance measurement of dark energy parameters in addition to that
provided by the SZE. The survey depth and breadth, in combination with
good atmospheric seeing, will also enable high signal-to-noise
measurements of the weak lensing shear caused by large-scale structure
and of the galaxy-shear cross-correlation, each of which provides new
constraints on the dark energy. In addition, measurement of the
evolution of the angular clustering of galaxies will provide an
independent probe of dark energy, using features in the clustering power
spectrum as standard rod distance estimators. Finally, through repeat
scanning of selected areas of the survey, the DES will obtain densely
sampled light-curves for \~1900 Type Ia supernovae and constrain the
dark energy through the classical redshift-magnitude relation.

Together, these powerful, complementary techniques will probe the dark
energy with unprecedented precision: individually they will probe the
dark energy equation of state parameter *w* (see Sec. 2.1) at the 5-15%
level; collectively they can in principle reach the few percent level.
It is important to emphasize that these are estimates of statistical
errors, assuming constant *w*, and do not yet include full accounting
for systematic errors. Moreover, parameter constraint forecasts
generally depend upon priors assumed for marginalized parameters as well
as on assumptions about whether and how *w* evolves. As a result,
extreme caution must be exercised in comparing the projected
cosmological parameter sensitivity of different experiments and methods,
and we will attempt to be explicit about the priors used in the
projections below. In all cases, we assume massless neutrinos, no
running of the primordial spectral index of the mass power spectrum, and
a fiducial model with non-evolving *w* = −1. More important than the
expected statistical precision is the fact that the different methods
the DES will use to probe dark energy are subject to *different*
systematic errors and cosmological parameter degeneracies (see, e.g.,
Fig. 2.3-2), so their inter-comparison should provide a gauge of the
systematic errors and a more robust final result.

As described in this chapter, these science goals can be achieved with a
moderately deep (\~24^th^ magnitude) survey in four optical passbands,
g, r, i, and z. The following chapters show that such a survey can be
completed using 30% of the telescope time over five years with a new 520
megapixel camera with a 3 deg^2^ field of view on the existing Blanco
4-meter telescope at CTIO. To achieve the requisite depth in the redder
passbands within the available survey time, we plan to use thick CCDs
with much greater quantum efficiency at long wavelengths than
conventional thinned devices.

In the following, we highlight the importance of dark energy for
fundamental physics and briefly describe the current state of dark
energy measurements. We then describe how each of the four
methods---cluster surveys, weak lensing, galaxy clustering, and
supernovae---will constrain the dark energy in the context of our
survey. The last section of this chapter describes the expected accuracy
of our photometric redshift measurements, a primary factor in
determining the science reach of these dark energy methods. An Appendix
describes the measurement of the cosmic shear sensitivity of the Survey.

**2.1 Evidence for Dark Energy**

In 1998, two research groups studying distant Type Ia supernovae
independently found direct evidence that the expansion of the Universe
is accelerating (Riess et al, 1998, Perlmutter et al 1999), arguably the
most important discovery in cosmology since the serendipitous detection
of the cosmic microwave background (CMB) radiation by Penzias & Wilson
in 1965. According to General Relativity, if the Universe is filled with
ordinary matter, the expansion should be slowing down due to gravity.
Since the expansion is speeding up, we are faced with two logical
possibilities, either of which would have profound implications for our
understanding of the fundamental laws of physics: (i) the Universe is
filled with a completely new kind of stress-energy with bizarre
properties (in particular, negative effective pressure), or (ii) General
Relativity breaks down on cosmological scales and must be replaced with
a new theory, perhaps associated with extra dimensions. For simplicity,
we will subsume both of these possibilities under the general rubric of
'Dark Energy', since in both cases the effects on the expansion of the
Universe can generally be described by that of an effective fluid with
equation of state parameter *w* = *p/ρ* \< −1/3 (we use units in which
the speed of light *c* = 1 throughout). For example, the dark energy
could be the energy of the quantum vacuum, that is, Einstein's
cosmological constant (in which case, *w* = −1), or it could signal the
existence of a new ultra-light particle with mass of order 10^-33^ GeV
or less; in either case, particle physics currently provides no
understanding of why the dark energy density should have the value that
would explain the current acceleration of the Universe.

Since 1998, independent but indirect evidence for dark energy has come
from several sources, most notably the combination of the CMB
temperature anisotropy pattern---which points to a spatially flat
Universe---and the evidence from large-scale structure and galaxy
clusters that the density of ordinary matter (mostly dark matter) is
about 30% that of a flat Universe. These studies indicate that the dark
energy comprises the remaining 70% of the energy density of the
Universe, Ω~DE~ ≈ 0.7, and that its equation of state parameter *w* \<
−0.75 at 95% confidence (this upper bound comes from combining
supernova, galaxy clustering, and CMB data, assumes *w* is constant, and
depends upon priors on other cosmological parameters, cf. Fig.2.1-1). In
order to pin down the nature of the dark energy and decide between the
theoretical alternatives, we need to measure *w* with greater precision
and determine whether and how it evolves with cosmic time. The Dark
Energy Survey, in combination with the SPT Survey, is designed to
determine *w* with a statistical (1σ) precision of order 5% (constant
*w*) and the dark energy density Ω~DE~ to within ± 0.01. In combination
with CMB data, the DES should also provide interesting constraints on
the evolution of the dark energy equation of state.

Figure 2.1-1 illustrates the complementarity of the current constraints
on dark energy arising from the CMB (WMAP), large-scale structure
(SDSS), and supernovae. While Figs. 2.1-1b and 2.3-2a show that the CMB
anisotropy itself does not strongly probe the dark energy equation of
state, it does accurately constrain the shape of the matter (mass) power
spectrum, considerably strengthening the dark energy reach of galaxy
clustering and weak lensing measurements. As Fig. 2.3-2a also shows, the
CMB constraints on dark energy are nearly orthogonal to those from
supernovae. The timing and scientific leverage of the Dark Energy Survey
make it complementary to two next-generation CMB mapping experiments
that will begin in 2007. The Planck Surveyor, a satellite that will
measure the CMB temperature and polarization anisotropy, will provide
constraints at the roughly 1% level on a range of cosmological
parameters that determine the matter power spectrum. The South Pole
Telescope, a ground-based, high angular resolution CMB mapping
experiment, will carry out an SZE survey of galaxy clusters over 4000
square degrees. The combination of the Dark Energy Survey with these CMB
experiments---especially the SPT---will provide even more precise
information about the dark energy.

![](media/image1.png){width="2.334722222222222in"
height="2.283333333333333in"}

Figure 2.1-1 Current 95% CL constraints from joint analysis of WMAP,
SDSS, and supernova surveys: (a) dark energy vs. dark matter density
(assuming *w* = −1); (b) dark energy equation of state vs. dark matter
density, assuming constant *w* and a flat Universe (Tegmark et al 2003).

**2.2 New Probes of Dark Energy**

Dark energy affects the history of the cosmic expansion rate, *H*(*z*),
over the last 10 billion years or so; this history determines the
observables upon which all dark energy probes are based. While
supernovae have provided the first direct evidence for dark energy (the
observable is the peak apparent brightness as a function of redshift),
in the last few years other techniques that complement the supernova
method have been undergoing rapid development. The Dark Energy Survey is
designed to exploit several of the most promising of these techniques,
including supernovae.

The first new method involves measuring the redshift distribution and
the evolution of clustering of massive clusters of galaxies. Galaxy
clusters are the largest collapsed structures in the Universe,
containing up to hundreds or thousands of individual galaxies. Because
the expansion rate of the Universe determines the cosmic volume as a
function of redshift as well as the growth rate of density
perturbations, the redshift distribution of clusters and its cosmic
evolution provide a sensitive probe of the dark energy equation of
state. Realizing this technique is the primary science driver for the
Dark Energy Survey.

A major project aimed at the cluster counting technique is now being
planned for the South Pole. The South Pole Telescope (SPT, John
Carlstrom, U. Chicago, PI), funded by the National Science Foundation,
is an \$18 million project that will start survey operations in 2007.
This project will use the SZE to detect galaxy clusters out to large
distances. The SZE is caused by inverse Compton interactions of CMB
photons and the hot gas (free electrons) that permeates clusters. These
interactions introduce a spectral distortion into the perfect blackbody
of the CMB. By precisely mapping the background radiation, the SPT will
provide a census of tens of thousands of clusters over a 4000 square
degree region south of declination δ = −30^o^. Because the SZE signal
from a cluster is a measure of the vast thermal energy in the electron
population, it is expected to be a robust indicator of cluster mass.

One advantage of the SZE is that it is a change in the spectral
distribution of the CMB rather than a source of emission, so it is
unaffected by the cosmological dimming that plagues studies of
high-redshift objects. This makes it a cluster selection tool that works
extremely well over a wide range of redshifts. However, once clusters
are detected in the SZE, one needs another method to determine their
redshifts, which are required to measure the redshift distribution and
clustering evolution. The most efficient way to obtain cluster redshifts
to the desired accuracy is by measuring the magnitudes and colors of the
galaxies they contain: all clusters contain a population of luminous red
galaxies, and the farther the cluster the redder the galaxies appear.
Thus, the SPT survey must be combined, over the same area of sky, with
an optical survey in several filters that can measure such color-derived
photometric redshifts. Currently, no telescope in the Southern
Hemisphere (which can survey the region of sky observable from the South
Pole) has an instrument capable of carrying out such a photometric
redshift survey with the requisite area and depth on a timescale of a
few years.

In addition to providing redshift estimates for the SPT clusters, the
Dark Energy Survey will provide an independent cluster counting probe of
the dark energy. The cluster counting method depends on having a good
estimate of the mass of each cluster. The SZE technique provides one
estimate of cluster mass, but optical observations of clusters provide
others: the more massive a cluster, the more luminous galaxies it
contains and the stronger its gravitational lensing effects on
background galaxy images. Current observations indicate that gas-based
probes of clusters (i.e., SZE or X-ray signatures) provide more accurate
estimates of cluster masses than optical techniques alone. Projection
effects are a major issue for lensing (e.g., White, etal 2002, Dodelson
2003) and, to a much lesser extent, for optical mass estimators (e.g.,
Lin et al 2003); projection is less problematic in SZE surveys. On the
other hand, radio emission from the nuclei of galaxies can interfere
with SZE cluster selection but does not affect optical cluster finding;
moreover, optical and lensing mass estimates do not depend on
assumptions about the state of the intracluster gas. Thus, SZE and
optical cluster finding and mass estimation are complementary; by
coordinating the Dark Energy Survey with the SPT survey, we can
cross-check mass estimates and control systematic errors.

A second new technique for probing the dark energy involves weak
gravitational lensing: by precisely measuring the shapes of distant
galaxies, we can infer how those shapes have been distorted due to their
light bending around foreground mass concentrations. The evolution of
the statistical pattern of these distortions---for example, of its
angular power spectrum---as well as of the cross-correlation between
foreground lensing galaxies and background galaxy shear, is sensitive to
the cosmic expansion history and thus to the dark energy (Hu 2002,
Huterer 2002). Weak lensing studies of dark energy require surveys that
cover a large area of sky from sites where atmospheric turbulence does
not cause excessive blurring of the galaxy images. The site at CTIO is
known to have excellent image quality.

The third technique exploits the dark energy leverage available in the
power spectra of the spatial distribution of galaxies. The matter power
spectrum as a function of wavenumber shows characteristic features, a
broad peak as well as baryon wiggles arising from the same acoustic
oscillations that give rise to the Doppler peaks in the CMB power
spectrum. With the Dark Energy Survey, we will be able to explore the
angular galaxy power spectrum in redshift shells out to z\~1.1. This
approach will provide cosmological information from the shape of the
power spectrum transfer function and physically calibrated distance
measurements to each redshift shell (Eisenstein, Hu, & Tegmark 1998,
Cooray et al 2001, Hu & Haiman 2003, Seo & Eisenstein 2003, Blake &
Glazebrook 2003).

The fourth approach to dark energy will be to revisit 40 deg^2^ of the
sky every third night, enabling the discovery of and providing
light-curves for a sample of approximately 1900 Type Ia supernovae at
redshifts 0.3\<z\<0.8. These SNe will provide relative distance
estimates that can be used to constrain the properties of the dark
energy---especially when combined with the other three approaches and
the CMB.

These four techniques have very different sources of systematic error
from one another. Because we do not yet know the fundamental limitations
of these different techniques, and because the problems raised by dark
energy are so profound, it is necessary to pursue all of the most
promising probes. The Dark Energy Survey does so within a single
project. Note that the three new techniques rely on an underlying
paradigm for the formation of large-scale structure, based on
gravitational instability of cold dark matter in the Universe, although
weak lensing measurements also yield a purely geometric dark energy test
with reduced sensitivity (Jain & Taylor 2003, Zhang, Stebbins, & Hui
2003, Hu & Jain 2003). Despite the on-going theoretical challenges in
fully understanding the formation and evolution of galaxies, recent CMB
and large-scale structure data have repeatedly shown that this paradigm
is robust, indicating that cosmological parameters can be confidently
probed in these new ways.

Below we describe each science component of the Dark Energy Survey in
greater detail.

**2.3 Galaxy Cluster Studies of the Dark Energy**

In recent years, it was recognized that large cluster surveys to
redshifts *z*\~1 can be used to study the galaxy cluster abundance and
its evolution and thereby deliver precise constraints on the amount and
nature of the dark energy (Wang & Steinhardt 1999, Haiman et al 2001). A
cluster survey carried out over large solid angle also constrains
cosmology through the spatial clustering of the galaxy clusters. The
correlated positions of galaxy clusters (encoded in the cluster power
spectrum *P~cl~(k,z)*) reflect the underlying correlations in the dark
matter; these correlations contain a wealth of cosmological information,
much like the information contained in the CMB anisotropy power
spectrum. We plan to use the cluster redshift distribution and the
cluster power spectrum as powerful cosmological probes to study the
density and nature of the dark energy.

The observed cluster redshift distribution in a survey is the product of
the comoving volume per unit redshift and solid angle, *d^2^V/dzdΩ,* and
the comoving density of detected clusters *n~com~*,

> ![](media/image3.wmf){width="4.486111111111111in"
> height="0.5277777777777778in"} (2.3:1)

where *dn/dM* is the cluster mass function, *H(z)* is the Hubble
parameter as a function of redshift, *D~A~(z)* is the angular diameter
distance, and *f(M,z)* is the redshift-dependent mass selection function
of the survey. Figure 2.3-1 shows a characteristic redshift distribution
for the SPT+DES cluster survey. The cosmological sensitivity comes from
three basic elements:

-   **Volume**: the volume per unit solid angle and redshift depends
    sensitively on cosmological parameters and has much in common with a
    simple distance measurement (such as that given by supernovae).

-   **Abundance Evolution**: the evolution of the number density of
    clusters, *(dn/dM)(z)*, depends sensitively on the growth rate of
    density perturbations, which is determined by the expansion rate
    *H(z)* and therefore highly sensitive to cosmological parameters.
    For example, the higher the matter density, the more rapidly
    perturbations grow at recent epochs, implying lower perturbation
    amplitude and therefore fewer clusters at high redshift. The number
    density also depends on the initial mass power spectrum.

-   **Mass selection function**: clusters are selected using some
    observable such as the integrated SZE flux (a measure of the thermal
    energy in the intracluster medium), galaxy number or light (a
    measure of the stellar mass in the cluster), or weak lensing shear
    (a measure of the projected mass density of the cluster). In
    general, all these observables are correlated with cluster mass. A
    flux-limited survey will pick out all clusters massive or luminous
    enough to lie just above the flux limit. Thus, the cluster selection
    function depends on the luminosity distance to that redshift, which
    depends on cosmology. The form of the selection function encodes the
    scatter about the characteristic mass---observable relation and at
    any redshift will vary from 0 for low-mass, undetectable clusters to
    1 for very high-mass, easily detected systems.

> ![](media/image4.png){width="3.625in"
> height="3.3993055555555554in"}Figure 2.3-1 The redshift distribution
> (blue) for the SPT+DES cluster survey for a fiducial cosmological
> model with *Ω~DE~* = 0.7*, w =* −1, and power spectrum amplitude
> *σ~8~* = 0.9. A particular realization of the model appears with red
> points and error bars. The green model with slightly different
> cosmological parameters can be excluded with 3σ confidence using a
> likelihood analysis of this data. The lower panel shows the deviations
> between the 3σ model and the fiducial model as a function of redshift
> in units of ν=∆/σ.

The cosmological sensitivity of the cluster power spectrum arises
primarily because there are features---including a break---in the power
spectrum that depend on the matter and baryon densities. These features
provide a standard ruler, calibrated by the CMB power spectrum. By
measuring the cluster angular power spectrum in a redshift bin, one
measures the angular scale of these features. Comparing the angular and
physical scale of these features provides direct angular diameter
distance information to that redshift (Cooray et al 2001). The
cosmological constraints from the cluster power spectrum are independent
of those from the cluster redshift distribution; taken together, they
constrain cosmology in a very robust manner (Majumdar & Mohr 2003b, Lima
& Hu 2004).

Several crucial components make possible precision studies of dark
energy using galaxy cluster surveys. First, the formation and evolution
of dark matter halos must be precisely modeled; fortunately, this is
well-understood theoretically and well-tested using N-body simulations
of structure formation (Jenkins et al 2001, Hu & Kravtsov 2002, Linder &
Jenkins 2003). Second, special-purpose surveys must be designed to
cleanly select clusters over a large range of mass and redshift---survey
completeness and contamination must be well understood when analyzing
the cluster redshift distribution. Third, photometric redshift estimates
must be available for large numbers of clusters---this drives the
synergy between the SPT and DES surveys. Finally, a mass---observable
relation must exist that can tie observable cluster properties (such as
the SZE flux or the galaxy light) to the underlying halo mass. The
combination of the DES and SPT surveys bring all these ingredients
together, making it possible to deliver robust constraints on the dark
energy from a sample of \~20,000 clusters.

![](media/image5.png){width="2.998611111111111in"
height="2.9138888888888888in"}![](media/image6.png){width="2.995833333333333in"
height="2.8666666666666667in"}

> Figure 2.3-2 Forecast 68% CL constraints on the dark energy equation
> of state parameter *w*, the dark energy density parameter *Ω*~DE,~ and
> the matter density parameter *Ω*~m~ for the SPT+DES galaxy cluster
> survey (blue). For comparison, forecasts for SNAP supernovae (green;
> Perlmutter & Schmidt 2003), current constraints from WMAPext (black
> dashed; Spergel et al 2003), and forecasts for Planck polarization
> (red) are shown. The cluster constraints in the left panel either
> assume a flat universe (solid blue) or solve for geometry and *w*
> simultaneously (dashed blue); the solid black contour shows
> approximate constraints from two years of DES data, using photometric
> redshifts to z = 0.8. The constraints arise from the cluster power
> spectrum, the cluster redshift distribution (assumed distributed
> uniformly out to *z* =1 for this figure), and 100 cluster mass
> measurements each accurate at the 30% level (1σ). Note that only the
> SNAP constraint includes a full estimate of systematic error.

Figure 2.3-2 shows forecasts for the dark energy constraints from the
SPT+DES cluster survey, compared with projected SNAP supernovae and
existing and projected CMB constraints, and assuming Gaussian initial
density perturbations as expected from inflation. The marginalized error
on the dark energy equation of state from SPT+DES clusters is δw ≈ 0.05
for a flat Universe. The complementary parameter degeneracies underscore
the gains one can achieve by carrying out both cluster surveys and
supernova distance measurements, as we plan to do in the Dark Energy
Survey. The 30% cluster mass measurements will come from a combination
of weak lensing constraints directly from the Dark Energy Survey, deep
pointed X-ray observations with Chandra or XMM-Newton, and perhaps
through dynamical mass estimates arising from spectroscopic studies of a
subset of the clusters. We emphasize that these forecasts include survey
self-calibration: the mass---observable relation and its evolution are
extracted from the survey directly (Majumdar & Mohr 2003a,b; Hu 2003,
Lima & Hu 2004). The precision of cosmological constraints suffers when
one requires self-calibration, but the accuracy is improved by
eliminating biases introduced by theoretically driven assumptions about
the expected form and evolution of the mass---observable relations. Put
another way, the constraints with self-calibration do incorporate an
estimate of the effects of a major source of systematic error in the
cluster measurements.

In calculating the forecasts shown above we have reserved considerable
cosmological information for cross-checking our constraints. As shown in
Equation 2.3:1, the redshift distribution involves an integral over the
mass function. Using in addition the shape of the mass function directly
would improve the cosmological constraints (Hu 2003), but with the
approach outlined here we can, at the end of the analysis of the
redshift distribution and cluster power spectrum, *predict* the cluster
mass function as a function of redshift. A direct comparison of the
theoretical mass functions for the best-fit cosmology and the observed
mass functions derived from the survey (in essence, the observed
luminosity functions, which can be converted to a mass function using
the parameters of the mass---observable relation) will indicate the
level of self-consistency---and effectively the level of accuracy---of
our analysis. These multiple, independent sources of information from a
cluster survey make it a particularly powerful probe of the dark energy.

Finally, we note that the constraints shown here and below (except as
noted) assume that the dark energy equation of state parameter *w* is
constant in time; if *w* evolves, then the corresponding constraints on
its present value, *w~0~*, are generally less stringent. On the other
hand, the SPT+DES cluster abundance, in conjunction with determination
of the mass power spectrum normalization, can provide constraints on the
evolution of *w* that complement those that will come from SNAP (Battye
& Weller 2003, Hu 2004). We discuss constraints on the time evolution of
*w* expected from the DES further below.

1.  **Optical Cluster Finding and Mass Estimates**

The classical method of identifying clusters is to search for large
aggregations of galaxies. Empirically, this method is justified by the
strong observed correlation between the distribution of mass and the
distribution of luminous galaxies on large scales. In the Dark Energy
Survey, finding clusters optically and measuring the number of luminous
galaxies they contain will provide estimates of the cluster abundance
and cluster masses that are independent of those from the SZE.

![](media/image7.png){width="2.4618055555555554in"
height="1.9902777777777778in"}

Figure 2.3-3: Optical image of a galaxy cluster at z = 0.15 (SDSS
collaboration).

![](media/image8.png){width="6.5in" height="1.8833333333333333in"}

> Figure 2.3-4 Galaxies in the red sequence from a cluster at z =1.27
> (van Dokkum etal 2001).

Clusters of galaxies can be identified optically by searching for
concentrations of galaxies with the same color: clusters exhibit a
population of red (elliptical or S0) galaxies that have remarkably
uniform colors (Gladders and Yee 2000). With increasing redshift,
cluster galaxies appear progressively redder, providing a basis for a
color or photometric estimate of cluster redshifts. A version of this
red-sequence technique for identifying clusters, called the maxBcg
algorithm, has been used in the SDSS out to redshifts *z* \~0.3−0.5
(Annis et al 1999, Sheldon et al 2001, Bahcall et al 2003; see
Fig.2.3-5). The maxBCG algorithm has been tested on N-body
simulation-based mock catalogs, populated with realistic galaxy
populations, out to these redshifts (Wechsler et al, in preparation).
 The use of a color-based selection technique reduces projection effects
significantly: virtually all massive clusters are found with the method,
and for M\>10^14^ *M~sun~*, the scatter between mass and *N~red~* is
less than about 30%.

![](media/image9.png){width="6.0in" height="3.515277777777778in"}Figure
2.3-5 An example of the maxBcg algorithm used to identify clusters in
SDSS imaging data. For galaxies in a small area of sky, *g-r* color is
plotted against apparent *i-*band magnitude. The dotted curve shows the
expected locus of the most luminous galaxies found in clusters, with
redshift increasing along the curve. This cluster is at z =0.1. Ellipses
indicate the 1, 2, 3σ expectation values for the most luminous galaxy in
a cluster at z =0.11, while the vertical dotted line shows the
luminosity limit used for the count of red cluster galaxies, *N~red~*.

We expect this method to work well out to higher redshift: cluster
galaxies have been found to have homogeneous colors up to and beyond *z*
= 1 (Ellis et al 1997, Stanford et al 1998; van Dokkum et al 2000, van
Dokkum et al 2001; Lidman et al 2003), and the red-sequence technique
has been applied to find clusters in this regime (Gladders et al 2003).
A variety of observational studies of galaxy evolution indicate that the
population of red galaxies as a whole, not just those in clusters, is
quite stable out to redshifts of *z* \~ 1, indicating that this
technique for cluster identification should be robust.

For the SPT survey, the observable used to statistically estimate
cluster mass is the SZE flux. For optically selected clusters, one can
use, e.g., total galaxy luminosity (e.g., Bahcall et al 2003, Lin et al
2004); for the remainder of this discussion, we will instead adopt the
number of red galaxies above a limiting luminosity, *N~red~*, as the
optical mass estimator, since it is straightforward to measure with the
maxBcg cluster finding technique (see Fig. 2.3-5).

Weak lensing measurements provide an additional method for calibrating
the relation between *N~red\ ~* and cluster mass (see, e.g., Fig.
2.4-2). To obtain high signal to noise, one stacks many clusters of a
given *N~red~* and photometric redshift interval and determines the mean
tangential shear profile, a technique used on a sample of early SDSS
data by Sheldon et al (2001). The mass scalings derived from this method
agree very well with those derived from spectroscopic velocity
dispersions (McKay et al 2004), an important cross-check on the method.
The Dark Energy Survey will allow us to build weak lensing vs. *N~red~*
scaling relations out to z = 0.7.  All three mass indicators derived
from optically-selected clusters (optical luminosity or number of red
galaxies, weak lensing mass, and cluster velocity dispersion) are being
extensively tested and cross-checked with clusters identified with
maxBCG in realistic simulation-based mock catalogs.  This has allowed
both tuning of the algorithm to get the most robust cluster selection
and direct comparison to dark matter halo masses, which allows us to
understand the systematic uncertainties inherent in each method.  

> ![](media/image10.png){width="4.75in"
> height="3.7979166666666666in"}Figure 2.3-6 A realization of the DES
> cluster redshift distribution from the Hubble Volume N-body
> simulation. The three curves show the distribution for different mass
> bins, where *M* is the cluster mass in units of solar mass. The red
> curve corresponds roughly to the detection mass threshold for SPT
> clusters (10^14^ *M~sun~*), while optical imaging can detect clusters
> down to lower masses, similar to that indicated by the black curve
> (10^13.5^ *M~sun~* or *N~red~* = 10).

**2.4 Weak Gravitational Lensing and Dark Energy**

The bending of light by foreground mass concentrations shears the images
of distant source galaxies. Dense mass concentrations such as galaxy
clusters induce a coherent tangential shear pattern that can be used to
reconstruct their surface mass densities. Larger scale structures with
lower density contrast also generate correlated shear, but with lower
amplitude---in this case one studies the shear pattern statistically, a
method known as cosmic shear or shear-shear correlations. Since the
foreground dark matter is associated to large degree with foreground
galaxies, one can also measure the angular correlation between
foreground galaxy positions and source galaxy shear, a technique known
as galaxy-shear correlations or galaxy-galaxy lensing.

These weak lensing techniques provide powerful probes of the dark energy
in the context of the Dark Energy Survey: the shear-shear and
galaxy-shear correlations depend on and therefore constrain the dark
energy density and equation of state. In addition, as noted above,
lensing provides statistical cluster mass estimates that can cross-check
SZE, X-ray, and galaxy-number-based mass estimators. Although
shear-shear and galaxy-shear correlations were detected for the first
time several years ago, the Dark Energy Survey, with its wide area
coverage, depth, and photometric redshift information, will exploit the
dark energy sensitivity of these techniques.

The shear-shear, galaxy-shear, and galaxy angular power spectra can be
expressed as projections of the corresponding three-dimensional power
spectra (e.g., Hu & Jain 2003),

![](media/image11.wmf){width="3.263888888888889in"
height="0.4722222222222222in"} (2.4:1)

where  denotes the angular multipole, *a*, *b* = (1, 2), *x*~1~ and
*x*~2~ denote the two-dimensional

angular galaxy (*g*) and shear (*γ*) fields, and *s*~1~ and *s*~2~
respectively denote the three-dimensional galaxy (*g*) and mass (*m*)
density fluctuation fields at redshift *z*. The weight functions *W*~1~
and *W*~2~ encode information about the galaxy redshift distribution and
about the efficiency with which foreground masses shear background
galaxies as a function of their respective distances.

The dark energy density and equation of state affect these angular power
spectra through the distance and weight factors and through the
redshift- and scale-dependence of the three-dimensional power spectra ,
, and . For a given set of cosmological parameters, the mass power
spectrum can be accurately predicted from N-body cosmological
simulations; the shape (scale-dependence) of is also well constrained on
large scales by WMAP data on the CMB anisotropy, and we include this as
a prior in the forecasts shown below. In addition to cosmology, the
power spectra involving galaxies, and , require a model for the *bias*,
that is, for how luminous galaxies are distributed with respect to the
dark matter. We describe the bias in terms of the 'halo model', with 5
parameters that determine how galaxies occupy dark matter halos; this
model is physically motivated and accurately reproduces the results of
N-body simulations that include gas dynamics.

To forecast constraints, we estimate the statistical errors on the
angular power spectra; for illustration, we focus on the shear-shear
spectrum, for which the uncertainty is (Kaiser 1992)

![](media/image12.wmf){width="2.4305555555555554in"
height="0.5416666666666666in"} (2.4:2)

where is the fraction of sky area covered by the survey, σ^2^(γ~i~) is
the variance in a single component of the (two-component) shear, and
${\overline{n}}_{A}$ is the source galaxy angular number density per sr.
The first term in brackets comes from cosmic variance, and the second,
shot-noise term results from both the variance in galaxy ellipticities
('shape noise') and random error in measuring galaxy shapes. Eqn.(2.4:2)
assumes the shear field is Gaussian; although this assumption breaks
down at large , the non-Gaussian variance is generally masked by the
shape noise term. In what follows, we only use information from
multipoles \<3000.

Eqn. (2.4:2) indicates that weak lensing places a premium on maximizing
the survey sky coverage and the surface density of source galaxies with
measurable shapes. We choose to rewrite the shot-noise term in
Eqn.(2.4:2) as (0.32)^2^/*n~eff~*, where the numerator is the empirical
shape noise for large, well-measured galaxy images, and is the source
galaxy density including noise weighting. In estimating the shear, the
ellipticity measurement of each source galaxy is relatively weighted by
the inverse noise, which has contributions from shape noise and shape
measurement error. In addition, the ellipticity of each galaxy is
corrected for PSF dilution by a factor that depends on the square of the
ratio of the galaxy size to the PSF---for small galaxies the correction
is large, and uncertainty in the correction factor means these galaxies
are further downweighted in the shear estimate. These effects are
incorporated into the noise-weighted galaxy density *..*

This effective or weighted number density is convenient because it can
be used with power spectrum noise estimates that assume the usual shape
noise amplitude.

To estimate for the DES, we studied a 900 sec CFH12K *I* band exposure
taken in median seeing of 0.63\'\' and artificially degraded the image
to different seeing values. Note that this is the same exposure time to
reach the DES i band survey requirement (chapter 3) and that the CFHT
mirror aperture is close to that of the Blanco. We measured the
ellipticities and sizes of the detected objects using an adaptive
weighting scheme that is nearly optimal for lensing measurements
(Bernstein and Jarvis 2002). . In the CFH12K image, the mean relative
galaxy weight per magnitude bin is essentially unity out to *I~AB~* =
21.5 and drops to about 0.5 at , which is the nominal 10σ magnitude
limit for galaxies. The effective source density for the DES *i* band
images is shown in the bottom panel of Fig.2.7-1 as a function of
seeing. An increase in seeing has two effects: (i) the PSF is a larger
fraction of the size of the faint source galaxies, so they receive less
weight due to the larger PSF dilution factor; (ii) the effective number
of CCD pixels per object is larger, increasing the sky background per
object and therefore the shape measurement error. Fig.2.7-1 shows that
for 0.9\'\' seeing, which is the assumed median for the DES, we expect
an effective source galaxy density of *=*10 arcmin, and we have adopted
this value in the analysis below. Note that this is smaller by about a
factor of two than the actual source density above the 10σ detection
limit of the Survey, because it includes weighting due to measurement
error, PSF dilution, and shear polarizability. The upper panel of
Fig.2.7-1 shows the shear sensitivity per component, which is just
evaluated at degrees. In comparison to the CFHT Legacy Survey, which
goes deeper over a smaller area of sky, the signal to noise on the shear
variance for the Dark Energy Survey should be larger by a factor of
three.

![](media/image13.wmf)The survey depth also determines the redshift
distribution of the source galaxies. In the estimates below, we used a
distribution with median redshift . This is consistent with the
distribution inferred from redshift surveys to , the expected depth of
the DES, and with models of galaxy counts. The actual source redshift
distribution for lensing will differ from that for a survey with this
flux limit because (a) we include source galaxies beyond the 10σ
detection limit, and (b) fainter galaxies contribute less weight. These
correction effects go in opposite directions, and we expect this adopted
median source redshift to be reasonably accurate.

> Figure 2.4-1 Upper panel: shear sensitivity for the Dark Energy Survey
> as a function of seeing, for 900 sec *i* band exposure. Lower panel:
> effective source galaxy density for lensing as function of seeing, for
> same exposure.

The three angular power spectra provide constraints on the
multi-dimensional parameter space that includes the halo and
cosmological parameters, and we forecast constraints on *w* and by
marginalizing over the others, assuming spatial flatness. We include
approximate WMAP CMB priors, specifically, 10% errors on *Ω~m~h^2^,
Ω~b~h^2^*, the primordial spectral index *n*, and the power spectrum
amplitude *A*^1/2^, but no CMB priors on any dark energy parameters. We
assume the source galaxies can be separated into 4 photometric redshift
bins of width ∆z = 0.28 out to z = 1.1, plus a fifth bin for all
higher-redshift galaxies. The lens galaxies are placed in bins of ∆z =
0.1 out to z = 1 and are included if they reside in halos of mass
greater than 10^13.5^ *h*^−1^ *M~sun~*. Although the shear-shear (cosmic
shear) spectrum is proportional to and thus independent of the halo
model parameters, it has the lowest signal to noise and is the most
sensitive to systematic errors in measuring galaxy shapes. At the other
extreme, the galaxy auto-power spectrum is most sensitive to
uncertainties in the bias model, but it has the highest signal to noise.
Fig. 2.4-2 shows the expected constraints from the Dark Energy Survey:
all 3 power spectra jointly determine *w* with a statistical uncertainty
of less than 4% and to better than 1%; more conservatively, cosmic shear
alone yields a 1*-σ* error on (constant) *w* of 6%. These numbers will
decrease with improved priors from Planck.

For comparison, Fig. 2.4-2 also shows approximate constraints expected
after two years of DES data. Using the survey strategy described in
chapter 4, the expected effective source galaxy density from two years
of data is 6.6 per square arcmin (as opposed to 10 for five years of
data). The noise in the shear measurements has been scaled up
accordingly; in addition, the source galaxy redshift distribution after
two years will be modestly shallower than for the full survey; this
effect has not been incorporated in the figure, leading to a small
underestimate of the two-year parameter errors.

> ![](media/image14.png){width="3.29375in"
> height="3.4715277777777778in"}

Figure 2.4-2 Forecast 68% CL constraints on *w* and from weak lensing in
the DES. Red: shear-shear correlations; green: galaxy-shear
correlations, with halo model parameters constrained by foreground
galaxy auto-correlations; blue: joint constraints from all three power
spectra. Dotted curves indicate approximate constraints expected after 2
years of data, according to the survey strategy described in Chapter 4.

The statistical accuracy of these parameter measurements, while
impressive, can only be reached if systematic errors are kept under
control. For shear measurements, the dominant systematic error comes
from residuals in correcting galaxy shapes for the effects of an
anisotropic point spread function (PSF), caused by optical and CCD
distortions, tracking errors, wind shake, atmospheric refraction, etc.
One uses the shapes of stars to measure, interpolate, and correct the
PSF, but the finite angular density of well-measured stars yields a
sparse sampling of the spatially and temporally varying PSF field. Our
experience with weak lensing measurements using the wide-field Mosaic II
camera on the Blanco 4-m telescope demonstrates that the PSF can be
accurately mapped and corrected to be circular across the field to less
than 0.5%. For the wider-field Dark Energy Camera and associated
corrector, an optical design with low, stable, and smoothly varying
distortion across the field of view is required. The galaxy-shear
correlations are less sensitive to these systematics than shear-shear
correlations, because PSF anisotropy tends to cancel out of the
azimuthally averaged tangential shear field measured around foreground
galaxies. Moreover, since the foreground galaxy auto-spectrum is
independent of the shear measurements, it provides an independent
cross-check on the shear systematics.

In addition to statistical measurements, weak lensing in the Dark Energy
Survey will yield low-resolution projected mass maps and mass profiles
for galaxy clusters, especially in the redshift range *z* ∼ 0.1-0.5. In
addition, the shear fields for clusters of given redshift and galaxy
number or SZE flux can be 'stacked' to yield a mean mass profile, useful
for calibrating cluster mass estimates based on optical galaxy counts or
the SZE. An example of a low-redshift cluster mass reconstruction using
the Blanco 4-m (with cumulative exposure time longer than for the DES)
is shown in Fig. 2.4-3 (from Joffre et al. 1999); the shear in the inner
regions, of order a few percent, is detected at 7σ.

![](media/image15.png){width="3.6354166666666665in" height="3.06875in"}

Figure 2.4-3 Reconstructed projected mass map for the *z* =0.05 cluster
Abell 3266 superposed on an *R*-band image with the BTC from the Blanco
4-m telescope. Contours are significance levels, and the image is 44
arcmin on a side; the galaxy source density used to reconstruct the mass
map was 15 per arcmin.

**2.4.1 Constraining Dark Energy Evolution with Clusters and Weak
Lensing**

The forecast constraints from the cluster survey and from weak lensing
shown so far have assumed the dark energy equation of state *w* is
constant in time. In fact, if *w* ≠ −1, then it is likely to evolve as
well; current constraints on the evolution of *w* are very weak, and
determining it will be an important goal of future dark energy projects.
It is convenient to parametrize the evolution by writing *w*(*a*) =
*w*~0~ + (*1−a*)*w~a\ ~*, where *w~a~* = − *dw/da*\|~0~ , subscript 0
denotes the present epoch, and the cosmic scale factor *a = 1/(1+z)*.
Fig.2.4-3 shows forecast constraints on *w~a\ ~* vs. *w*~0~ from weak
lensing and from the cluster survey (Hu 2004). Note that, for
illustrative purposes, the assumptions and priors used here differ
somewhat from those used above. In partcular, here the CMB priors on
parameters (now including dark energy parameters) are more aggressive,
coming from expected statistical errors from the Planck satellite. For
cosmic shear, we use the same range of angular scales for the analysis
as above; however, for the galaxy-shear measurement, here we include
lens galaxies in halos down to 10^12.5^ *h*^−1^ *M~sun~*, i.e., we push
the halo model down to lower masses, but we only include constraints
from angular multipoles \<1000, a more restrictive range than above.
For the cluster redshift distribution and mass function, here we include
all clusters more massive than 10^14.2^ *h*^−1^ *M~sun~* in 10 redshift
bins to z = 1. To show the effects of uncertainty in the mass-observable
relation, we show constraints for (i) perfect calibration of the
relation, (ii) allowance for uncalibrated power-law evolution of the
relation with redshift, and (iii) inclusion of self-calibration using
the cluster power spectrum (variance in counts in cells). For both weak
lensing and the cluster survey, the forecast (purely statistical)
constraint on the evolution of *w* is at the level of δ*w~a~* \~ 0.5.

![](media/image16.png){width="5.378472222222222in"
height="3.063888888888889in"}

Figure 2.4-3 68% CL constraints on evolution of the dark energy equation
of state *w~a\ ~* vs. *w*~0~ for (a) cosmic shear (\`shear-shear') and
galaxy-shear (\`galaxy-lensing') correlations and (b) the cluster
abundance. In these plots we marginalize over dark energy density Ω~DE~,
assuming a flat Universe, and assume Planck CMB priors on cosmological
parameters.

**2.5 Galaxy Angular Clustering**

The Dark Energy Survey (DES) will deliver a sample of over 300 million
galaxies extending beyond a redshift of one. On large scales, galaxy
clustering and its evolution should reflect the gravitational dynamics
of the underlying dark matter distribution. The ratio of the galaxy and
dark matter power spectra can be described by a redshift-dependent bias
factor, *b*^2^*(z),* that is theoretically expected to be
scale-independent on large scales, although its amplitude does depend on
the type (e.g., luminosity, color) of galaxy being studied. In the
linear regime, we can write the galaxy power spectrum as

$P_{\text{gal}}(k){µk}^{n}T^{2}(k;p_{i})g^{2}(z;p_{j})b^{2}(z),$ (2.5:1)

where the initial dark matter power spectrum from the early Universe
![](media/image17.wmf){width="0.34652777777777777in"
height="0.2222222222222222in"},
![](media/image18.wmf){width="0.3333333333333333in"
height="0.2222222222222222in"} is the scale-dependent transfer function
for dark matter
perturbations,![](media/image19.wmf){width="0.3055555555555556in"
height="0.2222222222222222in"} is the scale-independent perturbation
growth function, and the *p~i~* remind us that these functions depend
explicitly on cosmological parameters. In practice, we replace this
simple bias model with the more physically motivated and numerically
justified halo model mentioned above.

As with lensing, we will measure the galaxy angular power spectrum
within photometric redshift bins to probe the dark energy. The angular
power spectrum within a redshift shell can be written as

![](media/image20.wmf){width="2.236111111111111in"
height="0.4027777777777778in"}, (2.5:2)

where ![](media/image21.wmf){width="0.4583333333333333in"
height="0.2222222222222222in"} is the Bessel transform of the radial
selection function for redshift shell *i* (Tegmark et al. 2002, Dodelson
et al. 2002).

The transfer function has a characteristic break on a physical scale
corresponding to the horizon size at matter-radiation equality,
determined by the mean dark matter density, as well as small wiggles
associated with the effects of baryon acoustic oscillations on the dark
matter distribution. Within each redshift shell, the angular power
spectrum will reflect this characteristic break at some characteristic
angle. Thus, the angular power spectrum constrains a redshift-dependent
combination of the matter density and the angular diameter distance
(Cooray et al 2001). An estimate of the resulting dark energy
constraints, marginalized over the 5 halo parameters in each redshift
shell, assuming spatial flatness, and restricting information to angular
multipoles 50 \< *l* \< 300 where the halo model of bias should be
robust, is shown in Fig. 2.5-1. Here we have included statistical Planck
CMB priors on the power spectrum, employed 10 photometric redshift
shells to z = 1, and included galaxies in halos with mass greater than
10^12.5^ *h*^−1^ *M~sun~*.

![](media/image22.png){width="5.996527777777778in" height="5.85in"}

> Figure 2.5-1 (a) Galaxy angular power spectrum at z = 1 in a bin of
> width ∆z = 0.1; (b) 68% CL dark energy constraints for constant *w*,
> using the shaded band in (a) and 10 angular spectra; (c) constraints
> on dark energy evolution, marginalized over dark energy density Ω~DE~.
> Here, Planck CMB priors on cosmological parameters are assumed.

**2.6 Supernovae and Dark Energy**

Using supernova (SN) light curves to measure the expansion history of
the universe has rapidly become a foundational standard of cosmological
studies. Studies of nearby SNe (*e.g.*, Hamuy et al. 1996a) provided the
basis for the development of methods using Type Ia SNe as precision
distance indicators (*e.g.,* Hamuy et al. 1996b, Riess, Press, &
Kirshner 1996, Perlmutter et al. 1997), and the application of these
methods to studies of high redshift SNe provided the first direct
evidence for the accelerating expansion of the Universe (Riess et al.
1998, Perlmutter et al. 1999). Moreover, the dark energy constraints
from supernovae are complementary to those derived from the CMB,
large-scale structure, and, in the future, from lensing and cluster
surveys.

The methods used to extract information from SN light curves are now
undergoing rapid refinement and improvement. The sources of systematic
uncertainty are being addressed and either minimized or eliminated by
new measurement capabilities and larger samples. As this control of
systematic uncertainties improves, new supernova surveys successively
take advantage of this knowledge by performing more detailed and
controlled measurements on both the supernovae and the supernova
samples, lowering the statistical uncertainty to the improved systematic
limit.

With the Dark Energy Survey, we have the opportunity to make the next
step forward in this progression. Compared to the current generation of
supernova surveys (*e.g.*, ESSENCE on the Blanco telescope and the CFHT
SN Legacy Survey), we will have new measurement capabilities and a wider
field to collect larger numbers of supernovae over a wide range of
redshifts. The proposed instrument design will allow much better control
over the wavelength response of the entire photometric system. In
addition, the proposed detectors will allow much better throughput in
the redder wavelengths that are crucial both to measuring SNe at high
redshift and to controlling and quantifying the systematics related to
dust and intrinsic SN dispersion at lower redshifts.

Based on these new capabilities, we have designed a baseline supernova
experiment which uses approximately 10% of the time dedicated to the
Dark Energy Survey operations, assumed to be 30% of the telescope time
over a five-year period. The requirements of this design include the
production of a large number of well-sampled SN light curves in three
bands in an observing strategy that fits within the 5000 deg^2^ DES
survey area and survey strategy. Balancing spatial coverage with depth
to cover a wide range of redshifts (0.25 \< z \< 0.75 ), we have
selected nominal exposure times of 200s in *r*, 400s in *i*, and 400s in
*z*. These exposure times should give us reasonable signal to noise SN
light curves in these bands out to z \~ 0.75. We would use roughly one
hour per night over four months each year for five years. Each night we
would cover roughly one third of our total survey area, returning to the
same fields every third night. Each observation of a given field would
be taken in *r* and alternately in *i* and z; with this cadence, we
would obtain *r* band SN light curves sampled every third night, with
*i* and z band light curves sampled every sixth night. In total we would
cover 13 Dark Energy Camera fields or 40 square degrees of sky, a much
larger area than that covered by any current intermediate to high
redshift SN survey.

With this baseline design, we have run Monte Carlo simulations of the SN
survey, assuming that the Dark Energy Camera has roughly similar *r* and
*i* band response to that of the existing CTIO Mosaic II camera (a
conservative assumption) and with the improved z band response described
in Chapter 5. Folding these sensitivities in with the historical
weather, seeing, and other observational factors, we estimate that we
will identify more than 1900 Type Ia SNe (along with many SNe of other
types) over the course of the five-year program.

Using this large sample of well-characterized SNe, we can constrain dark
energy parameters such as *w*. Figure 2.6-1 shows the results of
propagating the simulated light curves through a sample analysis to
determine the resulting cosmological parameters. The panel shows the SN
results alone and combined with the existing large-scale structure
results of the 2dF survey. In combination with other DES dark energy
probes, these constraints will be significantly tighter than those hoped
for from the set of SN surveys currently underway.

These simulations have many assumptions folded in, most of them based on
the experiences of past and current SN surveys, some of which may not be
directly applicable to the DES SN survey. Most important among these is
the implicit assumption that we will know the types and redshifts for
all of the SNe in our sample through spectroscopic observations of the
SNe and/or their host galaxies. We will discover more than 3000 SNe of
all types in the planned DES SN survey, and *immediate* spectroscopic
follow-up of *all* of these SNe is likely to be impossible. We can,
however, rely on the host galaxy photometric redshifts generated by the
Dark Energy Survey itself, as well as complementary photometric redshift
measurements provided by other surveys which overlap the area covered in
the DES SN survey, such as the SDSS, the VIMOS-VLT Deep Survey (VVDS),
and the NOAO Deep Wide Field Survey (NDWFS). We estimate that we should
have photometric redshift estimates for more than 80% of the host
galaxies of the SNe we discover. In addition, host galaxy spectroscopic
redshifts can be obtained over a longer timescale. Our preliminary
studies indicate that the errors in photometric redshift are
anti-correlated with both the K-correction errors and the uncertainties
in the decline rate or width versus brightness relations. Based on these
estimates, we predict that the rms of our SN distance determinations may
increase from the \~0.15 magnitudes typical of well-studied samples with
follow-up spectroscopy to \~0.25 magnitudes with photometric redshifts
only.

We are nevertheless planning to propose for extensive spectroscopic
follow-up with instruments on 4m to 10m telescopes worldwide; given the
breadth of our collaboration, we will almost certainly be able to obtain
spectra of a significant fraction (\>25%) of the SNe in our sample and a
much higher fraction of host galaxy spectra. Existing spectroscopic
measurements of host galaxies from SDSS, VVDS, and NDWFS will of course
complement the spectroscopy we are able to obtain. This subsample of
spectroscopically observed SNe will not only provide lower uncertainties
for some fraction of our SN sample, it will also provide a control
sample against which we can compare the results of our analysis of the
SNe which lack spectroscopic observations. In particular, these studies
will allow us to gauge the level of contamination of the color-selected
Ia sample by other supernova types. Together, these efforts will
decrease the uncertainties in our derivation of cosmological parameters
from the "worst case" scenario relying on only the available photometric
redshifts. In the simulations portrayed in Figure 2.6-1, we have used an
rms value of 0.20 magnitudes to demonstrate the results which may be
possible from detailed analysis of a sample of SNe with a mixture of
photometric and spectroscopic redshifts.

> ![](media/image23.png){width="3.0in"
> height="2.203472222222222in"}Figure 2.6-1. Projected constraints on
> Ω~M~ and *w* from the five-year DES SN survey. A flat cosmology has
> been assumed. Red: the SN survey alone; blue: joint constraints from
> SNe + 2dF (Ω~M~ = 0.278 ± 0.042). Contours represent 1, 2, and 3 σ
> confidence levels. The curves at right represent the constraints on
> *w* after marginalization over Ω~M~.

It is important to note that our Survey Instrument would be one of the
primary instruments in the world for this type of supernova survey
during its years of operation. We expect that the current generation of
surveys will yield suggestive results and puzzles that will be addressed
by the DES SN work, so the baseline plan described above is very likely
to be updated with a significantly more sophisticated plan that builds
on what we know at the time we begin this effort. It is also significant
that many of the challenges we face in the DES SN survey, such as the
lack of spectroscopic information on the majority of SNe, are the same
as those which will be faced in the following decade when we attempt to
use the incredible samples of SNe which the LSST will identify
continuously throughout its operational lifetime. Just as the results of
the current generation of SN surveys will guide us in updating and
refining the strategies we will employ for the DES SN survey, so will
this survey provide crucial experience and guidance for deriving
scientific results from the plethora of SNe discovered by LSST.

## 2.7 Photometric Redshifts {#photometric-redshifts .unnumbered}

In order to achieve the dark energy scientific goals described above,
the Dark Energy Survey will need to obtain accurate galaxy photometric
redshifts to z \~ 1. This requirement is therefore a prime driver of the
design and strategy of the Dark Energy Survey discussed in subsequent
chapters. In the absence of spectroscopic data, redshifts of galaxies
may be estimated using multi-band photometry, which may be thought of as
very low-resolution spectroscopy. Though such photometric redshifts (or
photo-z's) are necessarily less accurate than true spectroscopic
redshifts, they nonetheless are sufficient for the science applications
we envision. Photo-z's may be obtained less expensively and for much
larger samples than is possible with spectroscopy.

There are two basic approaches to measuring galaxy photometric
redshifts. The first relies on fitting model galaxy spectral energy
distributions (SEDs) to the photometric data, where the models span a
range of expected galaxy redshifts and spectral types (e.g., Sawicki et
al. 1997). The second approach depends on using an existing
spectroscopic redshift sample as a training set to derive an empirical
photometric redshift fitting relation (Connolly et al. 1995). There are
advantages and disadvantages to each approach, as well as a number of
variants and hybrids of these basic techniques (e.g., Csabai 2003).
However, photometric redshift methods ultimately rely on measuring the
signal in the photometric data arising from prominent "break" features
present in galaxy spectra, e.g., the 4000Å break in red, early-type
galaxies, or the Lyman break at 912Å in blue, star-forming galaxies. The
key is to have photometric bands which cover such break features
throughout the redshift range of interest, so that the primary redshift
signal may be readily detected. Additional refinements in the
photometric redshift measurement then come from the strength of the
break features and the gross shape of the galaxy SED, as determined by
the photometric data on either side of the spectral break.

1.  **Photometric Redshift Simulations for Cluster Galaxies**

Cluster photometric redshift measurements are greatly facilitated by the
strength of the 4000Å break feature prominently seen in the spectra of
red cluster galaxies. This is illustrated in Figure 2.7-1, which shows a
red, elliptical galaxy spectrum at redshifts 0, 0.5, and 1, superimposed
on the *griz* filter bandpasses. The 4000Å break moves through the
different filters as the galaxy redshift increases. Measurements of the
relative galaxy fluxes through the different filters provide an estimate
of the observed wavelength of the 4000Å break and hence of the galaxy
redshift. This is demonstrated in Figure 2.7-2, where we compare
photometric and spectroscopic redshifts for a sample of SDSS red
galaxies, for which we are able to obtain photometric redshifts with a
scatter σ~z~ =0.03 out to redshifts z ≈0.6. However, at higher redshifts
we do not have such large samples of red galaxies available, and so we
need to rely on Monte Carlo simulations to assess the quality of cluster
galaxy photometric redshifts for the Dark Energy Survey.

![](media/image24.png){width="3.625in" height="3.4791666666666665in"}

> Figure 2.7-1 The spectrum of a red, elliptical galaxy is shown at
> redshifts *z* =0, 0.5, and 1, with the location of the prominent 4000Å
> break feature marked. The spectra have been vertically offset for
> clarity. Also shown are the *griz* filter bandpasses, arbitrarily
> normalized to the same peak value. Photometric redshift information
> for red galaxies comes primarily from changes in the relative galaxy
> fluxes in the different filters as the 4000Å break moves to longer
> observed wavelengths at higher redshift.

In our cluster Monte Carlo simulations, we adopt the local cluster
luminosity function, and luminosity-mass and number-mass relations
(within the virial region set by *R*~200~), found by Lin, Mohr, &
Stanford (2004). We take the cluster galaxies to evolve with redshift
according to a passively evolving elliptical galaxy model from the
Pegase-2 SED library (Fioc & Rocca-Volmerange 1997). The Pegase-2 model
is designed to match the colors of SDSS red galaxies at low redshifts
and is a 13 Gyr model that incorporates self-consistent star formation
until the onset of galactic winds at z=3. A flat cosmology, with , , and
*h*=0.7 is used. The cluster luminosity function faint-end slope is
fixed at α=−1.1, but we take the halo occupation number to evolve with
redshift as (1+z)^γ^, where γ=1 (Lin et al. 2004; Kravtsov et al. 2004).
Using these assumptions, we can simulate the *griz* magnitudes of
cluster galaxies as a function of redshift. Expected photometric errors
are calculated by appropriately scaling the *S*/*N* relative to the 10σ
magnitude limits *griz* = 24.6, 24.1, 24.0, 23.65, and by adding in
quadrature a 2% error due to photometric calibration uncertainty
(although these flux limits go beyond the survey requirements in g and r
given in Chapter 3, the survey plan in Chapter 4 indicates they should
be achievable; the enhanced depth in these bluer filters does not have a
large effect on cluster galaxy photometric redshift errors). For the
purposes of photometric redshift estimation for an entire cluster, it
suffices to calculate the total light in each filter contributed by all
cluster galaxies down to the 10σ limit in the z band, which maximizes
the number of detected cluster galaxies. Here, we are considering the
case in which we know a cluster exists along a particular line of sight
based on the SZE detection from the SPT survey, so we are not concerned
with the optical cluster detection significance, as we would be for a
purely optical cluster finding technique. We will, however, scale our
total light estimates by a conservative "loss" fraction of 0.5 to
account for various effects, such as contaminating field galaxies,
cluster blue fractions, and the like.

We use a simple least-squares template fitting method to determine
photometric redshifts for clusters of mass 1.0x10^14^ and 2.5x10^14^
*M~sun~*. For each case, 20,000 mock clusters are generated and
distributed uniformly over the redshift range z = 0−2. Fig. 2.7-3 shows
the results and demonstrates that the Dark Energy Survey should provide
robust photometric redshifts for such clusters to z≈1.3. For these
clusters, we find a small photo-z scatter (68% limit) σ~z~ ≤ 0.02, with
the tails of the photo-z error distribution extending no more than about
0.05 in redshift. At higher redshifts, z \> 1.3, color degeneracies
become important, and the tails of the error distribution become larger,
though the 68% limit scatter is still typically σ~z~\<0.1. Note also
that removing our assumed halo occupation number evolution gives a
change in photo-z quality comparable to that seen in reducing the
cluster mass by the factor of 2.5 shown in Fig. 2.7-3. In practice, we
will need to acquire a spectroscopic redshift training set (see below)
in order to empirically measure the correct cluster galaxy SED to use
for our final photo-z calibration, especially at redshifts *z \>*0.7
where existing galaxy cluster data are very sparse. However, our current
simulation results do show that the expected Dark Energy Survey flux
limits are sufficient to provide robust photometric redshifts for our
cluster-based dark energy science goals.

![](media/image25.png){width="2.625in" height="2.625in"}

> Figure 2.7-2 Photometric and spectroscopic redshifts are shown for a
> sample of SDSS red galaxies, for which a photometric redshift scatter
> σ~z~=0.03 is obtained.

# ![](media/image26.png){width="3.9277777777777776in" height="3.9277777777777776in"} {#section .Section-Heading}

Figure 2.7-3 Photometric redshift results for the 1.0x10^14^ and
2.5x10^14^ *M~sun~* galaxy cluster Monte Carlo simulations; see text for
the details. The red lines show the median difference between
photometric and true redshift, the blue lines show the 1σ scatter (68%
limits), and the green dashed lines are set at ∆*z* = ±0.02 and ±0.1 to
guide the eye.

### Photometric Redshifts for the General Galaxy Population

We also aim to obtain photometric redshifts for the general galaxy
population, beyond red cluster galaxies, in particular to facilitate
dark energy measurements using weak lensing, galaxy clustering, and
supernovae. Such photo-z's are necessarily less accurate than those
possible for cluster galaxies, as we must consider a much broader
distribution of galaxy SEDs. Nonetheless, as we shall show, good
photometric redshift measurements out to *z* ≈1.3 can be achieved for
the general galaxy population in the Dark Energy Survey.

We demonstrate this with a Monte Carlo simulation, using galaxy SEDs
from the Coleman, Wu, & Weedman (CWW; Coleman et al. 1980) sample. We
use 20,000 mock galaxies, uniformly distributed in redshift (z = 0−1.5),
magnitude (*i* = 23-24), and SED type (E to Im), and noise is added as
appropriate for the Dark Energy Survey *griz* flux limits given in
Chapter 4. Note that this simulation is only intended to estimate the
photo-z uncertainty (averaged over galaxy types) as a function of
redshift, rather than to approximate a realistic galaxy redshift
distribution. In particular, we apply a simple least-squares template
fitting method that only uses color information; hence no flux-redshift
correlations are used, as would be the case for polynomial photo-z
fitting.

Our results are shown in Figure 2.7-4, where we find that the
photometric redshift scatter (68% limits) is typically σ~z~=0.1-0.2 for
these galaxies with *i* = 23−24 (note that *i* =24.0 is the required
survey 10σ limit). Though not shown in the figure, the photo-z scatter
does of course improve at brighter magnitudes, where we find σ~z~≈0.05
at *i* = 22. Note that the photo-z trends vs. spectroscopic redshift in
Figure 2.7-4 are in general well behaved, except at the lowest
redshifts, *z\<*0.3, where the photometric redshift is scattered
systematically high. This is likely a consequence of the lack of a
constraining filter blueward of the 4000Å break at these low redshifts.

We have also checked our photo-z results using an empirical, deep galaxy
sample, the publicly available ground-based *VRIz* photometric data
obtained by Capak et al. (2004) in the GOODS/HDF-N area, combined with a
training set of 1800 spectroscopic redshifts (down to the DES depths)
from the compilations of Wirth et al. (2004) and Cowie et al. (2004).
The *VRIz* photometry serves as a best-effort approximation to *griz*,
and we add noise to the original *VRIz* photometry in order to match the
Dark Energy Survey depths. We derive photo-z's using polynomial fitting
and find similar photo-z results as for our simulated galaxies. In
general, it will be important to understand in detail the scatter and
biases in our photometric redshifts by carefully measuring the photo-z
error distribution using a large spectroscopic redshift training set.
Two such large redshift surveys, the VIMOS VLT Deep Survey (VVDS; Le
Fevre et al. 2003) and the Keck DEEP2 Survey (Davis et al. 2002), are
both currently in progress. These surveys should provide spectroscopic
redshifts sufficient to calibrate general galaxy population photo-z's
down to the Dark Energy Survey requirement limit of *i* =24. In
particular, the VVDS will obtain about 100,000 redshifts, and it is also
being carried out from Chile. All the VVDS fields will be accessible to
*griz* imaging using the Blanco 4-m, enabling us to derive detailed
photometric redshift calibrations for the Dark Energy Survey.

> ![](media/image27.png){width="3.2104166666666667in"
> height="3.2104166666666667in"}
>
> Figure 2.7-4 Photometric redshift results for the general galaxy
> population Monte Carlo simulations, for the magnitude range *i* =
> 23-24; see text for details. The red lines show the median difference
> between photometric and true redshift, the blue lines show the 1σ
> scatter (68% limits), and the green dashed lines are set at ∆*z* =
> ±0.1 and ±0.2 to guide the
>
> eye.

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**3. From the Science Program to the Technical Requirements**

Our science program was outlined in the previous Chapter and is
summarized below. In this Chapter we show how the science program
evolves into technical requirements for the Dark Energy Survey
Instrument. The science requirements given below are unlikely to change;
however, the technical requirements and the choices that we have made to
reach them (e.g. number of filters) represent a reference design of the
Dark Energy Survey Instrument. This is not a final design; further
analysis may indicate better solutions are possible.

**3.1 Science Goals**

Our overarching science goal is to measure *w*, the dark energy equation
of state, to a precision of order δ*w* ≤  5% using four separate
techniques:

1.  **Cluster Survey:** In this method we measure the cluster redshift
    distribution and the cluster power spectrum. Cluster mass estimates
    come indirectly through the measured bias of the cluster power
    spectrum, and they also come directly from SZE, weak lensing, and
    cluster galaxy observables. To accomplish our goals, we must be able
    to locate and measure the redshifts of clusters of galaxies out to z
    \>= 1, because this is the expected extent of the coordinated South
    Pole Telescope SZE cluster survey. The cluster redshift distribution
    and power spectrum are sensitive to dark energy as probes of the
    volume and growth function. The survey must cover large solid angle
    to enable the cluster power spectrum measurement and to deliver
    large cluster samples. The survey must have multiple bandpasses for
    precision photometric redshifts, and faint limiting magnitudes to
    increase the number of galaxy photometric redshifts available for
    each cluster.

2.  **Weak Lensing:** The weak lensing of background galaxies by
    foreground mass sheets, correlated with itself (shear-shear
    correlations) or with foreground galaxies (shear-galaxy
    correlations) is sensitive to dark energy as a standard ruler and
    growth function probes. The DES Instrument must be able to measure
    the weak lensing signal from large scale structure in several
    redshift bins with small and stable instrumental point spread
    functions. Large solid angle is required to probe the shear field
    over the largest possible range in physical scales.

3.  **Galaxy Angular Power Spectra:** Measurement of the angular power
    spectra of galaxies in several redshift bins can also put
    constraints on w, because it is a standard ruler probe. To
    accomplish this measurement, we must have a stable photometric
    calibration, cover large solid angle to probe a wide range of
    physical scales, and have faint limiting magnitudes to increase our
    sample size and probe to redshifts beyond z\~1.

4.  **Supernovae:** The apparent magnitude of Type Ia supernovae
    provides a standardizable candle luminosity distance measurement.
    The DES Instrument will be used to measure light curves of more than
    1900 supernovae out to z = 0.8. The observations must cover a
    moderately large solid angle repeatedly to obtain lightcurves of the
    SNe. These SN observations must be taken in multiple (at least
    three) bandpasses to provide colors of the objects, both for
    reddening measurements and SN type discrimination based on colors.
    Excellent red response is necessary to obtain adequate signal for
    higher redshift SN lightcurves. Detailed knowledge of the system
    passbands as a function of wavelength is required for accurate k
    correction calculations.

**3.2 Science Requirements**

The primary science requirement is to provide photometric redshifts for
the galaxy clusters detected in the South Pole Telescope SZE survey. The
data required to meet this primary requirement will facilitate
coordinated studies of optically selected cluster samples, weak lensing,
and the galaxy angular power spectrum. The observing efficiency and
scientific return of an extensive photometric survey like the Dark
Energy Survey benefits from the addition of the time domain SN Ia
component, which will be pursued even during non-photometric time.

**3.2.1 Filters**

To meet the photometric redshift requirement we will bracket the 4000Å
break signal in the elliptical galaxy spectrum typical of cluster
galaxies. At z=0, the blue side limit is 400 nm. Redshift moves this
restframe value redward. At z=1, the 4000Å break is at 800nm. Precision
photometric redshifts require a filter redward of the observed break. We
thus need bandspasses from 400nm to 1000nm. For our reference design, we
adopt the SDSS filter set, g, r, i, z, with z as the fiducial bandpass.
This choice allows us to leverage the existing body of work done with
these filters to aid in analysis and calibration of DES data.

**3.2.2 Limiting Magnitudes**

Measuring accurate photometric redshifts of the clusters near the
detection threshold in the SPT SZE survey at redshifts z\~1 drives us to
faint magnitude limits. Reaching the cluster photometric redshift
accuracy of δz=0.02 requires that we measure photometric redshifts of at
least ten galaxies in clusters of mass M=2x10^14^M~o~ at z=1. A limiting
absolute magnitude corresponding to a 0.5*L*\* galaxy suffices for
photometric redshifts of this precision, and a side benefit is that
photometry of cluster galaxies to this depth enables efficient optical
cluster detection as well. The absolute magnitude of a \* galaxy at z=0
is, in the i-band, M~i~ = -21.0 (Blanton et al 2003, redshift and
evolution corrected). At z=1 such a galaxy would, if it followed the
passively evolving old stellar population model appropriate for high
luminosity cluster galaxies, have a z-band magnitude *z*=23.3. We
require 10σ observations, for the photometric redshift techniques. Data
from both the CTIO Blanco 4-m and the CFHT 3.6m show that 10σ is near
where the differential number counts turn over, an indication that it is
close to the completeness limit. We will go 0.3 mags deeper in the
fiducial band in order to safely construct a flux-limited sample of
galaxies. We therefore require a limiting magnitude of z=23.6.

The minimum apparent magnitudes in the remaining bandpasses follow by
considering the redshifted, passively evolving spectrum of a cluster
galaxy. The limiting magnitude required is that for a \* galaxy at the
redshift where the 4000Å break leaves the bluer filter and enters the
redder filter. The 4000Å break leaves the g, r and i bandpasses at
redshifts of 0.35, 0.65, and 1.0 respectively. The corresponding
limiting magnitudes are shown in Table 3.1. We will see later that the
requirements on the g and r limiting magnitudes are easy to meet.

Table 3.1 Limiting Magnitudes

  ---------------------- ----------- ----------- ----------- ---------------
  Filter                 g           r           i           z

  Mag (10σ)              22.8        23.4        24.0        23.6
  ---------------------- ----------- ----------- ----------- ---------------

**3.2.3 Photometric Calibrations**

We plan to calibrate the final coadded map to 1%; however, meeting the
science goals for the photometric redshifts only requires photometric
calibrations accurate to the level of 2%. Our science goals also require
that a calibrated spectrum, convolved with the DES system response, will
predict the observed magnitude to 2%. We have no requirement on the
calibration of single images, but we estimate that these can be
calibrated to 10% using the USNO B2 star catalogue.

**3.2.4 The Survey Area**

The DES will cover a total of 5000 sq. degrees in the Southern Galactic
Cap. Figure 3.1 and Table 3.2 show the specific areas. Below we describe
the motivation for these choices.

Table 3.2 Dark Energy Survey areas

+-------------------+-------------------+--------------+--------------+
| Overlap target    | Right Ascension   | Declination  | Area (sq.    |
|                   | (deg)             | (deg)        | deg.)        |
+-------------------+-------------------+--------------+--------------+
| SPT               | -60 to 105        | -30 to -65   | 3988         |
|                   |                   |              |              |
|                   | -75 to -60        | -45 to --65  |              |
+-------------------+-------------------+--------------+--------------+
| SDSS Stripe 82    | -50 to 50         | -1.0 to 1.0  | 200          |
+-------------------+-------------------+--------------+--------------+
| Connection region | 20 to 50          | -30 to --1.0 | 800          |
+-------------------+-------------------+--------------+--------------+

**3.2.4.1 SPT overlap**

The primary science requirement for the DES cluster survey measurement
of *w* is to image an area overlapping that of the observations of the
CMB by the South Pole Telescope (SPT). This will enable a combination of
clean cluster selection from the SZE data from SPT and the photometric
redshifts from the DES. The SPT collaboration will map the CMB in a 4000
sq-degree region observable from the South Pole. We plan to cover the
same area in our survey. The largest area near the top in Figure 3.1
corresponds to the SPT overlap region.

**3.2.4.2 Photometric Redshift Calibration Area**

All of the science goals require precision photometric redshifts. To
achieve the required limits on systematic uncertainties (\<2%), a
calibration sample of roughly 1000 redshifts per 0.1 bin in redshift is
needed in addition to the DES data. Our strategy is to make use of
public spectroscopic datasets in areas of the sky that we can image. The
largest and most diverse of these datasets is that of the SDSS Southern
Survey which has 100,000 redshifts down to i=20 and out to z=0.5, in
combination with the 2dF-SDSS survey, which has 10,000 redshifts of red
galaxies out to z=0.75. This same area of sky also contains fields from
both the VIRMOS-VLT Deep Survey (8 sq-deg with spectroscopic redshifts
to magnitude I~AB~ = 22.5, and 2 sq-deg with redshifts to I~AB~ = 24)
and the Keck Deep2 Survey (2 sq-deg with spectroscopic redshifts to
R~AB~=24). These surveys will deliver tens of thousands of spectroscopic
redshifts extending over our redshift range of interest. The Southern
Survey area is the narrow region at the bottom of Figure 3.1.

![](media/image28.png){width="2.4916666666666667in"
height="2.4916666666666667in"}

Figure 3.1 Map of the galactic dust in the South Galactic Cap. The South
celestial pole is at top. Our reference design survey footprint is
inside the green line. The Large Magellanic Cloud is visible in the
upper right corner of the survey area.

**3.2.4.3 Connection Area**

Precise photometric redshift determination also requires the same
photometry in the spectroscopic calibration areas as in the main survey
areas. To address this we plan to image a contiguous 800 square degree
area between the SPT area and the SDSS Southern Survey area. This area
is optimally placed for providing targets and optical follow up for the
optical and radio telescopes in Chile, including the ACT SZE survey, and
the observations of the ALMA array.

**3.2.5 The Point Spread Function**

The weak lensing science goal drives the requirements on the point
spread function (PSF). Measurement of the effects of large scale
structure on the shapes of galaxies requires shear measurements at the
1% level. As discussed in the appendix to Chapter 2, the density of
galaxies useful for weak lensing measurements is a strong function of
PSF size. To achieve our goals we need median seeing to be 0.9" FWHM.
Using the current prime focus imaging instrumentation the Blanco 4-m
delivers typical seeing between 0.8" and 1.1". The best seeing in our
survey will be determined by a combination of site and dome conditions
as well as the optics of the corrector. We will impose an upper limit of
\~1.1" such that only data with seeing better than 1.1" will be included
in the survey coadded images.

Stars in the science images are used to measure the PSF. In a single 100
second exposure we can reach magnitude 21 at S/N=100. At this magnitude
there are about 2000 stars per sq-degree. Based on experience with SDSS,
roughly a quarter of these 2000 stars will be away from frame edges,
cosmic rays, and other stars and galaxies. Using the plate scale of the
Blanco 4-m (55 microns/arcsec) this corresponds to a little more than 1
useful star per sq-cm (=9 sq-arcmin). With this information, a map of
the PSF and possible variations over the focal plane will be generated.
One can sample the PSF on scales smaller then 1 sq-cm by averaging over
several images but only if the instrumental PSF is constant against, for
example, gravity induced flexure changes or thermal effects.

To measure the weak lensing distortions at 1%, we require less than 0.1%
change in PSF over 1 sq-cm areas delivered at the focal plane, and that
the flatness variations in a CCD over 1 sq-cm areas produce changes in
the size of the PSF due to focus effects of less than 0.1%. These goals
are aimed not at keeping the mean value the same, but at keeping changes
smoothly varying and only on scales larger than 1 sq-cm.

**3.2.6 Science Requirements Summary**

The science goals discussed in Chapter 2 results in the following list
of requirements:

1.  5000 sq-degrees in the South Galactic Cap

    a.  4000 sq-degrees to overlap the SPT survey area

    b.  200 sq-degrees to overlap redshift surveys for photo-z training
        samples

    c.  800 sq-degrees connection region optimal for CTIO and Atacama
        telescopes

2.  Photometric redshifts for clusters to z=1.0 with δ*z* ≤ 0.02

    a.  SDSS g,r,i,z are sufficient

    b.  g=22.8, r= 23.4, i=24.0 z=23.6

    c.  Photometric calibration to 2%, enhanced goal to 1%

3.  A stable and small PSF

    a.  Seeing \< 1.1" FWHM with median ≤ 0.9\'\' FWHM

    b.  PSF stable to 0.1% over a 1 sq-cm (9 sq-arcmin) area of focal
        plane

**3.3 Technical Specifications**

To meet the science requirements listed above we must make choices that
strike a balance between the science requirements and what is
technically feasible. Below we present our reference design for the
CCDs, the integration time of the images, readout time and read noise,
the pixel size, image quality required for the optics, and the size of
the field of view of the corrector and camera. These lead to a set of
technical requirements which are used to guide the design of the DES
corrector and camera.

**3.3.1 CCDs**

For the purposes of developing a reference design for the DES
Instrument, we have chosen the LBNL CCDs. The technical specifications
are described in more detail in Chapter 5. The critical features for
this discussion are that they are 2k x 4k 4-side buttable devices with
15 micron pixels and a QE\>50% in the z band. They have been clocked at
250 kpix/sec with 7e- read noise. If the corrector optics preserve the
plate scale of the Blanco 4-m prime focus at 55 microns/arcsec, then
each CCD has an active area of 170 sq-arcminutes and each 15 micron
pixel covers 0.272". We note that the thick LBNL CCDs will also
eliminate fringing effects, thus simplifying data reduction and
improving signal-to-noise for the z and i band.

**3.3.2 Integration Times**

To achieve our science goals we require a signal-to-noise of 10 at the
limiting magnitudes. The integration time is the total of all individual
exposures. The calculation of the exposure time depends on the mirror
area, the throughput of the system, the read noise of the instrument,
the pixel size, the sky background, and the area over which the object
is spread. Table 3.3 and the discussion below describe our calculation
of the integration times needed to meet our science goals.

The Blanco 4-m telescope has an effective light collecting area of 10.3
sq-meter. We will use the SDSS filters, but our reference design for the
corrector has two more optical elements than the SDSS design. In Table
3.1 we give CCD quantum efficiencies (QE) and the transmission of the
optics and filters (T dλ / λ) using those from the SDSS, except that we
assume (1) 5 times better QE in z as we are using the LBNL CCDs, (2) a
wider z filter, and (3) an additional overall transmission factor of
0.9, allowing for the losses in transmission due to the extra two
optical elements. We assume the camera has a read noise of , and that
the pixel size is 0.27"/pixel.

The SDSS has measured the sky brightness in each of the filters, albeit
at a site distant from CTIO (but consistent with measurements made
there), and we tabulate it in Table 3.1. The sky brightness varies in
time and a half magnitude increase in the sky brightness results in a
quarter magnitude decrease in the limiting magnitude. The solar cycle
causes sky brightness variations on the order of 0.5 magnitudes, and the
next solar maximum is roughly 2012. We note that the sky brightnesses in
Table 3.3 are from SDSS data taken in the years near the last solar
maximum. Table 3.3 also shows the effect of observing in "gray time"
using data from the SDSS Photometric Telescope. This is time within 45
degrees of a moon at a phase fuller than half. The table shows that we
can image through the i and z filters in such conditions. Note that we
are in the process of compiling griz sky brightness data for CTIO from
the southern standard stars NOAO survey program of Smith and Tucker. A
small initial data set from that program indicates that the CTIO sky
values are consistent with the much more extensive SDSS sky data
measured at APO; we will have more thorough CTIO data available shortly.

The sky brightness contributes to the noise in galaxy magnitudes and
colors in proportion to the area of the aperture one uses to measure the
magnitudes. We assume the seeing at the Blanco 4-m is 1.0", and use an
aperture of 1.7" diameter, as is appropriate for the measurement of the
colors and magnitudes of faint galaxies. The resulting minimum
integration times are listed in Table 3.3.

Table 3.3 Limiting Magnitudes to Integration Times

  --------------------------------------- ------- ------- -------- --------
  Filter                                  g       r       i        z

  Limiting magnitude (10σ)                22.8    23.4    24.0     23.6

  CCD QE                                  0.65    0.85    0.65     0.5

  Filter and optics T dλ / λ              0.18    0.14    0.13     0.18

  Sky brightness (mag/sq-arcsec)          21.7    20.7    20.0     18.7

  Sky Brightness 45^o^ from moon          19.7    19.7    19.25    18.5

  Minimum integration times (sec)         27      130     900      1600

  Adopted integration times (sec)         400     400     1200     2000
  --------------------------------------- ------- ------- -------- --------

  -----------------------------------------------------------------------

  -----------------------------------------------------------------------

The last row gives our adopted integration times for the reference
survey strategy; please see Chapter 4 for a detailed discussion. The
increased exposure times in the g and r filters reflect our photometric
calibration strategy which will use multiple survey tilings and a
minimum exposure time of 100 sec.

We demonstrate next that our reference design choice of LBNL CCDs, with
their high z-band QE, is primarily motivated by our science requirement
to obtain a complete sample of cluster galaxies at redshift one. Recall
from Section 3.2.2 that our z-band depth was chosen to safely construct
a complete, flux-limited sample down to z=23.3, the z-band magnitude for
a 0.5*L*\* passively-evolving cluster galaxy at redshift one. In
particular, we compare the z-band depth for LBNL CCDs against that of a
commercially available e2v deep depletion device (CCD42-90), which has a
factor of 2 less QE integrated over the z-band, and hence a 0.38
magnitude shallower z-band depth for the same exposure time.
Specifically, we use the deep z-band image (3.9 hr total exposure on the
Subaru 8.2m) of the Hawaii HDF-North (Capak et al. 2004, AJ, 127, 180),
and add Poisson noise to simulate images taken using an LBNL CCD and an
e2v CCD, for the same adopted integration time from Table 3.3. We then
run SExtractor photometry on the original and noise-added images, and
compare the source number counts in the noise-added images against those
in the original image. Figure 3.2 shows the resulting plot of
completeness vs. z-band magnitude for the LBNL and e2v cases. We see
that the source counts start to become incomplete immediately faintwards
of the indicated 10σ limits, and that the completeness has dropped to
about 0.75 by 0.4 magnitude beyond the 10σ limits. We further see that
the e2v case barely meets the completeness requirement for a 0.5*L*\*,
redshift one cluster galaxy, while the LBNL case safely meets the
requirement with about a 0.4 magnitude margin. Note that such a margin
will be needed for any survey areas observed with worse than median
seeing (i.e., \> 0.9" FWHM) or with fewer than the full number of
tilings (see Chapter 4).

![](media/image29.png)

Figure 3.2. Comparison of source count completeness vs. z magnitude for
the cases of an LBNL CCD (red-solid) and an e2v CCD (blue-dashed), both
for the same total survey z-band exposure time. The vertical magenta
(long dash) line shows the z magnitude of a redshift one, 0.5*L*\*
cluster galaxy.

**3.3.3 Read Noise**

One of the components of the exposure time calculation is the read noise
of the devices. Because the collecting area of the Blanco 4-m is large,
the time required to reach the regime where sky noise dominates over
read nose is quite short. Read and sky noise add in quadrature. To meet
our science goals we require that the read noise be a factor of 2
smaller than the sky noise so that in the quadrature sum, the effect of
read noise is less than a 10% increase above pure sky noise. The g band
has the darkest sky (thus smallest sky noise), and with our 100 sec
exposures, a read noise of 15 e- will meet this requirement. The CCDs we
have chosen for our reference design have been clocked at 250 kpix/sec
with a corresponding read noise of 7 e-. An additional contribution from
the front end electronics (system noise) will add in quadrature to this.
State of the art instrumentation can achieve \< 5 e- read noise from the
front end electronics. We choose 10 e- as our technical goal for the
total read noise. In practice, following modern design techniques, the
system noise will most likely be low enough to allow a slower scan
readout generating images limited by the lowest readout noise attainable
with these CCDs of 2-3 e-/pixel.

4.  **Point Spread Function**

Our science goals encourage us to obtain the smallest possible PSF. The
delivered PSF is a combination of atmospheric and dome-induced seeing,
the optical design, mechanical alignment and stability, as well as the
telescope tracking (guiding and focus). The seeing at the Blanco 4-m
with the existing Mosaic II imager and its corrector is typically
between 0.8" and 1.1", and the instrument on the best nights can deliver
seeing as small as 0.6" FWHM; the site delivers considerably better than
this. We set as a technical goal that the combination of factors within
our control should not contribute more than 20% to the PSF width in
ideal seeing of 0.6" (so that the effective seeing is 0.72"), and 10% to
the PSF in typical seeing of 0.9". This translates into a requirement on
the delivered PSF from the combination of these effects of ≤0.4" FWHM
everywhere on the focal plane, for bandpasses of i and z which will be
used for weak lensing. Note also that pixels \<0.288" will then satisfy
2.5-pixel Nyquist sampling in ideal conditions, when the effective
seeing is 0.72".

As the technical design of the DES instrument progresses we will perform
detailed finite element analyses of the various instrument components
and of the entire telescope structure. We will use these analyses to
help us minimize the various contributions to the PSF due to mechanical
alignment and stability issues, including static optical alignment
errors, construction errors in the lenses and the mirror, quasi-static
errors (such as uncorrected mechanical deflection of the prime focus
cage and imperfect focus), and possibly others. 

**3.3.4.1 Telescope tracking and focus**

The delivered PSF can be strongly affected by the telescope tracking and
focus. The TCS incorporates an interpolated lookup table model (pointing
model) to compensate for flexure and irregularities in open-loop
telescope tracking. Guide cameras follow stars near the object being
observed, sending tracking corrections to the TCS every second and
closing the tracking servo loop thus guaranteeing good quality images
for exposures of up to 1800 seconds in most observing programs. Focus
changes at the Blanco telescope resulting from changes in ambient
temperature are corrected manually by the observer.

As noted before, the existing prime focus camera delivers seeing
typically between 0.8" and 1.1". Unguided focus sequences, which are
designed to locate current best focus, show a median seeing of 0.7" to
0.8". It is tempting to conclude that the telescope performs well on the
10 second timescale of a focus exposure sequence, but less well in the
several hundred second exposures of a typical observing run. This would
probably be erroneous as many observers will happily and appropriately
refocus the telescope should the seeing improve, but not when it
degrades when it would be a waste of observing time. The existing
tracking and guiding algorithms are not perfect and effort will be put
into refining them where possible.

As described in Chapter 5, for our reference design we have chosen to
devote space on the focal plane to both guide and focus CCDs. We will
read out the focus CCDs with each science exposure, while the guide CCDs
will be read out on a 1 sec time scale. This has the advantage that the
guiding and focus measurements are made by CCDs that are rigidly
attached to the focal plane and improves on the current imager by
allowing for continual monitoring of telescope focus.

As discussed above (Section 3.2.5), we expect \~25 well measured stars
(S/N = 100) per science CCD in a 100 sec. science exposure. If the focus
chips were 10 times smaller than the science CCDs (i.e., focus chip area
= 17 sq-arcmin), 2 stars per CCD would be expected and we believe this
to be sufficient. For our reference design we assume the guide exposures
will be 1 second. One can measure sufficiently accurate centroids to
guide from a star of S/N=20. If we take the sky noise to be 16e- and the
read noise over the star image to be 20e-, then we need a star producing
1000 e- in 1 second. This is magnitude 19 for the r or i bandpasses,
which have surface densities of Σ=0.2 per sq-arcmin (5x10^-5^ per
sq-degree) near the galactic pole. Assuming the stars are randomly
distributed on the sky, then the probability of finding no useable guide
stars within a guide camera of solid angle Ω is There are about 2000
fields in our survey. We choose a target probability of , where there
would be about a 10% chance of having one field without a guide star.
This suggests that we need 25-50 sq-arcmin of guider to be assured of at
least one guide star in each field.

**3.3.5 Field Size**

The size of the field necessary to meet the science goals depends on
many aspects of the survey. The primary constraint is to cover the 5000
sq-deg DES area in the time available for the DES during the 5 year
period. However, consideration must also be given to factors of weather,
survey strategy, and operational efficiency, among others. Research into
the technical feasibility of obtaining optical components has resulted
in few hard limits, for example, the availability of blanks for elements
up to 1.2 m in diameter.

An order of magnitude calculation shows the essentials. We are proposing
to use 30% of the Blanco 4-m time over 5 years, resulting in a survey of
525 nights. Our supernova program will take 10% of the time, leaving 473
nights for the imaging survey of 5000 sq-degrees. This requires an
acquisition rate of 10.6 sq-degrees per night. The nights in the
Southern summer will be around 8 hours long, giving a 10.6 sq-degree/8
hour rate. Assuming half the nights will be unusable due to weather then
gives 10.6-sq-degrees/4 hours. Our total exposure time on a given field
will be of order an hour, so the camera must cover 2.7 sq-degrees.

A careful analysis (see Chapter 4), incorporating overheads, a
four-bandpass calibration strategy, and a sophisticated weather model,
calculates a need for a camera whose single image tiles cover an area of
3.0 sq-degrees. This is realized in our optical design which has a 2.2
deg. diameter field of view.

**3.3.6 Requirements for the DES corrector**

Here we summarize how the science requirements and technical choices
listed above drive the design of the corrector for the DES. The
resulting corrector design is presented in Chapter 5. We recognize that
the camera will be used for a variety of purposes by the general
astronomical community and the corrector has been designed with this in
mind. Any changes in requirements beyond those required for DES that
have a significant cost impact will require that those costs be borne by
an organization other than the DES Collaboration.

1.  The full wavelength range will be 390 to 1100 nm. The design shall
    be optimized for performance within individual passbands of 390-540,
    560-680, 690-820, and 820-1100 nm. It is allowed that the focus
    and/or scale may change among passbands.

Basis: These are the passbands needed for measuring the photometric
redshifts of elliptical galaxies from z=0 to 1. The passbands match the
SDSS except that the longest wavelength has been extended. The overall
passband matches the optimal response of the LBNL CCDs.

2.  The pixel scale shall be 17.7 - 19 arcsec/mm (average over focal
    plane if distortion is present). The field of view shall be no
    smaller than 2.2 degrees.

Basis: The pixels will be 15 microns. The scale corresponds to 0.265 -
0.29 arcsec/pixel (the same as the existing Blanco). The larger value is
the scale at the prime focus before any correction. It gives a
resolution of 2.5 pixels for an image size of 0.72 arcsec (the target
minimum PSF). Coarser scale impacts the ability to do weak lensing
science. The angular field size is the minimum needed to complete the
SZE and supernova surveys within the allotted time \[30% of nights for 5
years.\] At the smallest pixel scale, the size of the field is 447 mm.

3.  The images shall have a polychromatic D80 \< 0.64 arcsec, for a
    merit function that averages over all wavelengths in a rectangular
    bandpass, for the bandpasses specified in requirement 1 at all
    locations on the focal plane. It is also a goal that the
    monochromatic D80 be achieved for all wavelengths 390-1100nm
    everywhere in the focal plane.

Basis: This value of D80 corresponds to a FWHM of 0.4 arcsec. From
raytrace spot diagrams, we find roughly that D80 = 1.59 \* FWHM where
FWHM is 1-D. For a Gaussian, D80 = 1.53 \* FWHM, D80 = 3.59\*sigma, and
FWHM = 2.35\*sigma. A FWHM of 0.4" is the target contribution to the
final image PSF from all non-atmospheric causes.

4.  No atmospheric dispersion correction (ADC) shall be incorporated.

Basis: The need for an ADC is not compelling. The maximum allowed
differential refraction is 0.8 arcsec peak-peak across the bandpass of a
filter. This corresponds to a D80 of 0.64 arcsec (actually a little
less); for an azimuthally-averaged PSF, the equivalent 1-D sigma is 0.17
and the 1-D FWHM is 0.40 arcsec. The driving filter is SDSS g. The
maximum airmass is 1.49. The declination for such an object on the
meridian is -78 deg. This is more than required to image the South Pole
Telescope survey area (declination \> -75 deg). The longest exposure
time in the g band will be 100 sec. The longer-wavelength filters are
much less affected.

The following requirements are driven by technical needs.

5.  The focal plane shall be flat.

Basis: It is easier to design and construct a mosaic array with a flat
vs. curved focal plane. Reference design studies show that no
significant gains are to be had by allowing the focal plane to be
curved.

6.  The operating temperature shall be -5 C to +27 C

Basis: Numbers taken from CTIO website.

7.  The maximum diameter of any optical element shall be less than
    1300 mm. The leading optical element shall be no more than 2300 mm
    in front of the focal plane.

Basis: These are the mechanical constraints placed by the available
space in the current design for the prime focus assembly. Smaller
elements are desirable, if possible, for cost reasons. It is not thought
that a diameter of 1300 mm provides any serious procurement problem.

8.  The preferred glass type for all elements is fused silica.

Basis: The glass type(s) should be chosen subject to the constraints of:

a)  lens blanks can be procured of the diameter and thickness required
    by the optical design

b)  the glass type meets the performance requirements of the design

c)  durability and manufacturability

d)  low coefficient of thermal expansion

e)  low radioactivity

f)  short procurement time

g)  fused silica seems to meet most if not all of these requirements.

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9.  Transmission shall be uniform in wavelength (10% peak-peak
    variation) over the full wavelength range specified in requirement
    1.

Basis: We do not want to compromise performance due to the choice of a
glass that has reduced transmission at either extreme of wavelength,
particularly at the blue end. The number 10% is a soft target. It is
thought that \"optical quality\" fused silica would meet this
requirement handily.

10. A 15 mm thickness filter shall be accommodated in the design. It is
    desired that it be located in a region of the corrector that has a
    minimum airspace whose size is the diameter of the filter plus 51
    mm, but otherwise be as close to the focal plane as possible. Within
    that airspace, the filter shall be placed 10 mm away from the
    optical element closest to the focal plane. The filter will be made
    of fused silica.

Basis: The filter must be thick enough to be mechanically sturdy. Sag
due to gravity is thought to be immaterial on the optical design for
this thickness. It is expected that filters will be mounted vertically
in the prime focus cage and will be rotated 90 degrees and translated to
insert them into the optical path. The airspace is needed to accommodate
the rotation of the filters. There is no requirement to accommodate
filters of different thicknesses.

11. A 25 mm minimum thickness glass window that is integrated with the
    vacuum dewar of the camera shall be included in the design. The
    glass type may be specified as part of the design. The window shall
    be located 40 mm in front of the focal plane. The window may have
    power. The material shall be chosen for low radioactivity. Fused
    silica meets this requirement.

Basis: The thickness is the minimum needed to support the window against
the air-vacuum pressure interface. A 25 mm thick flat plate has a safety
factor of 4.3 (at sea-level). Radioactivity generates spurious charge
events in the CCD and must be minimized. If the window has power, the
thickness of the window at its thinnest point shall be 25 mm. The
distance from the focal plane is determined by requiring that the
diameter of a ghost image of a star from the focal plane formed by the
nearest optical element be sufficiently large that the surface
brightness is a small fraction of that of the night sky.

The curvature of the window induced by the air-vacuum pressure
differential must be computed and its impact on the optical design
assessed. It is thought that the sag could be of order 60 microns, which
would require such an assessment.

12. Space for a shutter mechanism shall be provided with thickness
    51 mm. The location is not critical but should be as close to the
    focal plane as feasible.

Basis: Closer to the focal plane means a smaller beam size. Space for
the shutter is constrained in the prime focus cage.

13. Ghosting images shall be computed assuming a reflectivity for the
    CCDs in the focal plane of 15%, a reflectivity from the back of the
    dewar window and the front surface of the first element of the
    corrector of 2%, and all other surfaces of 0.8%. The variation in
    intensity of the exit pupil ghost image reflected back to the focal
    plane shall be no more than 2.5%.

Basis: Ghosting impairs one\'s ability to do flatfielding and creates
artifacts that must be modeled and subtracted from data as part of
routine data processing. The figure of 2.5% is not a hard one, but it is
known that ghosting properties can vary significantly for minor changes
in other figures of merit for a particular design. Ghosting is minimized
by minimizing the curvatures of those surfaces close to and concave to
the focal plane. The reflectivities are nominal values expected for the
LBNL CCDs, MgF2 coatings for two surfaces that must be hard to withstand
environmental conditions, and Sol-Gel coatings on all other surfaces.

14. Within the constraints of the above requirements on performance, the
    optical design shall minimize the total cost of construction.

Basis: Costs are driven by the glass type, spherical vs. aspherical
surfaces, details of lens shape (e.g. thickness), the total number of
elements (both w.r.t. procurement and to mounting complexity), the
delivery schedule, and the availability of qualified vendors.

**3.3.7 Science and Technical Requirements Summary**

Below we summarize the science requirements and the corresponding list
of technical requirements we have chosen for the DES instrument.

**Science Requirement:** Image 5000 sq-deg in the South Galactic Cap in
30% of 5 years

**Technical requirement**: 3 sq-degree camera with ≥ 2.2 deg FOV

**Science Requirement**: Measure galaxy cluster photometric redshifts to
z=1.0 with δ*z* ≤ 0.02.

**Technical requirements**:

a.  SDSS g,r,i,z filters covering 400nm to 1100nm

b.  Limiting magnitudes g=22.8, r= 23.4, i=24.0 z=23.6

c.  QE \> 50% in the z band

d.  Photometric calibration to 2%, enhanced goal to 1%

e.  Read noise \<10 e-

**Science Requirement:** A small and stable PSF

a.  Seeing \< 1.1" FWHM with median ≤ 0.9\'\' FWHM

b.  PSF stable to 0.1% over a 1 sq-cm (9 sq-arcmin) area

**Technical requirement**:

a.  Pixel size and optical plate scale which give \<0.3"/pixel

b.  Optical, mechanical and telescope tracking which contribute \< 0.4"
    FWHM

> across focal plane in the i and z bands

c.  Focus chip area \> 17 sq-arcminutes

d.  Guide chip area \> 25-50 sq-arcminutes, 1 Hz readout rate

**Technical Requirements on the DES Corrector**

Considerations of the conditions at the site and technical feasibility,
combined with the science and technical requirements listed above,
result in the following specifications for the DES Corrector:

1.  The full wavelength range will be 390 to 1100 nm.

2.  The pixel scale shall be 17.7 - 19 arcsec/mm (average over focal
    plane if distortion is present). The field of view shall be no
    smaller than 2.2 degrees.

3.  The images shall have a polychromatic D80 \< 0.64 arcsec
    (corresponding to a FWHM \< 0.4") averaged over all wavelengths
    listed in requirement 1 at all locations on the focal plane. It is a
    goal that the monochromatic D80 should be achieved for all
    wavelengths 390-1100nm everywhere in the focal plane.

4.  No atmospheric dispersion correction shall be incorporated.

5.  The focal plane shall be flat.

6.  The operating temperature shall be -5 C to +27 C

7.  The maximum diameter of any optical element shall be less than
    1300 mm. The leading optical element shall be no more than 2300 mm
    in front of the focal plane.

8.  The preferred glass type for all elements is fused silica.

9.  Transmission shall be uniform in wavelength (10% peak-peak
    variation) over the full wavelength range specified in requirement
    1.

10. A 15 mm thickness filter and space for changing filters shall be
    accommodated in the design.

11. A 25 mm minimum thick glass window that is integrated with the
    vacuum dewar of the camera shall be included in the design. The
    window shall be located 40 mm in front of the focal plane. The
    window may have power. The material shall be chosen for low
    radioactivity.

12. Space for a shutter mechanism shall be provided with thickness 51
    mm.

13. Ghosting images shall be computed. The variation in intensity of the
    exit pupil ghost image reflected back to the focal plane shall be no
    more than 2.5%.

> 14\. Within the constraints of the above requirements on performance,
> the optical design shall minimize the total cost of construction.

# Survey Strategy (WBS 1.3)

This section describes the elements of the Reference Design for the Dark
Energy Survey. It includes the survey observing strategy, the
photometric, astrometric, and photo-z calibration strategies, the survey
data simulations, and testing strategies for the data production system
and the data analysis system. The reference design is not intended to be
the final design, as we are still evaluating a number of technical
choices, but it serves as a vehicle to illustrate where we are in our
thinking and as a working model to guide our evaluations. Section 4.1
presents the observing strategy, section 4.2 presents the plans for
calibrating the data, and sections 4.3-4.5 present the plans for the
simulations that we will use to test the data management systems and
tune the science analysis software.

### Survey Strategy Work Breakdown Structure

There are six level three WBS elements in survey strategy, shown in
Figure 4.1. The first is the confusingly named survey strategy, which
covers two lower level elements: 1) the survey science requirements and
technical consequences, and 2) the survey observing strategy. Chapter 3
described the survey science requirements and the derived technical
requirements. The survey strategy WBS element concerns itself with the
creation and the maintenance of these documents. Chapter 4 describes the
survey observing strategy, and the WBS element concerns the creation and
implementation of this observing program.

The second level three element is survey calibrations. This covers three
lower level elements: 1) photometric calibration, 2) astrometric
calibration, and 3) photometric redshift calibration. All three are
necessary for the attainment of our science goals.

The third level three element is survey data simulations. We intend to
simulate a large fraction of the survey data. We intend to do this
because it will help us understand our instrument, help us understand
the universe we are observing, and help us understand the analyses we
will undertake.

The fourth level three element is the mock data reduction challenge,
which is aimed at taking the simulations of the survey that we produce
sending them through the data management system. This tests both the
data management system and our ability to deal with its output.

The fifth level three element is the mock data analysis challenge, which
is aimed at taking the output of the mock data reduction challenge and
sending it through our science analysis codes. This is, in some sense,
the core deliverable of the science working groups: analysis codes that
are ready and waiting for the data as they roll in. Our window of
opportunity is quite long, but there is no reason we cannot be ready to
spend our first summer working the science and not the analysis codes.
This WBS is thus a way to organize the activities of the collaboration
scientists interested in analysis towards a deadline.

The sixth level three element is survey operations, which is aimed at
carrying out the photometric calibration activities, the quality
assurance activities, and the survey tracking activities. We will
develop this plan in the future, but here we are focused on construction
and commissioning activities and will not describe this element further.

![](media/image30.wmf){width="3.9402777777777778in"
height="5.575in"}Figure 4.1 Organization of the Dark Energy Survey
Planning Project

## Survey Observing Strategy (WBS 1.3.1)

Large scale astronomical surveys aim to produce large sets of
homogeneous data that enable high precision statistical astronomy.
Homogeneity of data is the core strength of these surveys, but it does
not come easily nor freely; it must be planned for and thought must go
into the strategy for acquisition of the data.

### Overview of Survey Strategy

The observing strategy design goals are to

-   Efficiently attain the required depth

-   Minimize the photometric calibration errors, and

-   Obtain scientifically powerful data at natural points in the survey

    -   Year 2, at the time of the co-added data release

    -   Year 5, at the end of the survey

We will present two strategies that span the space of reasonable
strategies satisfying the design goals, evaluate them, and derive a
reference strategy from them. We focus on reasonable strategies, but it
is easy to imagine strategies that meet some design goals at the expense
of others. The maximally efficient survey strategy would be to point to
a given piece of sky, image it once in a given bandpass, and continue on
until all 4 filters are done. This strategy, unfortunately, leads to
deeply imaged islands, with little overlap and tends to maximize the
photometric calibration error across the survey array as well as maximum
cosmic rays in the data. A strategy better thought through but only
somewhat less unfortunate aims at complete photometric depth through
multiple tilings at the expense of area. This strategy leads in year 2
to a survey of 2000 sq-degrees, but the realities of observing large
areas of sky lead either to disjoint regions or in a long thin
rectangle. Unfortunate strategies are common, but the space of
reasonable strategies, strategies that meet survey goals with reasonable
certainty, is relatively compact.

The science case clearly makes the argument that area of the sky is of
primary importance. It is useful to recall that the time to reach a
given signal to noise[^1] goes from linear in time for source noise
dominated objects to square root in time for sky background noise
dominated objects. Essentially all of the objects of interest in our
science case are faint. A few tens of seconds exposure is sufficient to
reach high signal on the sky, and thus to reach the magnitude
characteristic of the transition from quick increase in depth in the
linear regime to slow increase in depth in the square root regime. For a
4 meter class telescope this characteristic magnitude is \~22^nd^
magnitude and is cosmologically interesting. Even short exposures over
the entire survey area would allow us to embark on our science program.

We are thus led to our first design decision, that area is more
important than depth[^2]. We will aim to image the entire survey area in
each bandpass in the first year. This guides the survey strategies
towards those strategies that cover the entire area once or more per
year.

Our second design decision flows from considering the photometric
calibration: we adopt tilings of the survey area as the core means of
obtaining the required photometric calibration. Large overlaps between
neighboring images allow the reduction of systematic errors by comparing
the same source observed in the different images. Images taken on
different nights allow the reduction of absolute calibration error
through root N averaging. Taken together, we are led to the idea of
tilings of the sky obtained on different nights and different years,
with half camera scale offsets between the tilings.

These two design decisions enable us to make substantial progress in the
survey design. Much of the rest of the constraints, as the landscape and
the surroundings constrain the architecture of a building, come from
considering the geometry of the survey area as seen from South America
and from the statistics of the weather at the Cerro Tololo site in
Central Chile. First though, we will consider the tilings of the survey.

### Tiles and Tilings

The survey area will be imaged multiple times and how the images fit
together determines what use we can make of them. In the interest of
obtaining high quality calibrations we plan to fit the images together
as tiles in a tiling. A tiling is the unique covering of the plane, or
an area on a plane, using shapes that can fit together without overlap.
These shapes can be irregular or the tiling can use more than one shape,
but for the purposes of mapping the sky a tiling that uses one shape, a
shape that approximates a regular shape, is most useful. The fact that
the sky is seen as a sphere makes the tiling an interesting problem.

![](media/image31.jpeg){width="0.9993055555555556in"
height="0.8326388888888889in"} **Tilings:** Telescope fields of view are
circular and the diameter of the circle the Dark Energy Camera (DECam)
corrector provides is 2.2 degrees. Circles cannot tile the plane, as
they either leave gaps or overlaps, though the overlapped circles form a
"tessellation" which does cover the plane. If the goal is to make a
homogeneous map of the sky, we see that the overlaps do not contribute
to the covering, so they are irrelevant other than to the cost of the
instrumentation.

![](media/image32.png){width="0.8744094488188976in"
height="0.3125984251968504in"}![](media/image33.png){width="0.8570866141732284in"
height="0.32716535433070865in"}![](media/image34.png){width="0.8070866141732284in"
height="0.2637795275590551in"}The three regular shapes that do tile the
plane are the triangle, the square, and the hexagon, and all of these
obey the rule that the angles at any vertex add to 360 degrees, the key
to the proof that there are only three. Hexagons are special in another
way: any partition of the plane into regions of equal area has a
perimeter at least that of the regular hexagonal honeycomb tiling[^3].
The perimeter of the CCDs on the focal plane of the DECam approximates a
hexagon by design. There exist tilings of the sphere using hexagons with
a handful of pentagons: the climate modeling community (e.g., Randall et
al 2003) has developed algorithms and software that use these tilings.

![](media/image35.png){width="0.8423611111111111in" height="0.875in"} In
detail, the actual DECam CCD layout forms a non-regular tile, though one
which still tiles the plane. There are some overlaps as half of few CCDs
project past the perimeter of the tiling area and these overlaps do not
contribute to the tiling. The tiling is slightly larger than the
inscribed hexagon, but its most salient difference from a hexagon is
that it is not a filled tile. The tiles will tile the plane, but the
CCDs, because of dead zones around them, do not fully fill the array,
thus leading to interesting effects.

The focal plane array consists of closely packed CCDs abutting on four
sides, yet because of the 1 mm guard ring around the edge of each LBNL
CCD, the tile of an individual image is sparse at the 10% level. The
implication of a sparse array is that a complete tiling has an uncovered
fraction that equals the inactive area of the tile. A second tiling will
cover this area usefully, but its own inactive area leaves a different
area without new coverage. The process is shown in Figure 4.2.

The area not covered is always the dead area fraction, 10%. The area of
sky with less than the full number of exposures accumulates at a rate of
N times the inactive area. Thus at 5 tilings half the area has the
targeted 5 tilings, while half the area has 4 tiling coverage. At 10
tilings, the entire area is covered by 9 tilings. As N becomes large,
the difference in exposure between areas covered N times and the area
covered N-1 times becomes small. The statistics of the coverage
distribution, shown in Figure 4.3, is slightly more complicated because
of the small areas of overlap between adjacent pointings in a single
tiling.

![](media/image36.png){width="2.0in" height="1.7375in"}

![](media/image38.png){width="2.0in"
height="1.7666666666666666in"}Figure 4.2 The effect of a sparse tile on
the sky coverage. The figure on the left shows a single tiling. Pink is
the area imaged, black is area covered more than once by CCD area
extending beyond the tile figure, and white shows area uncovered by an
image. The tile pattern is the reference design focal plane array
layout. The figure in the middle shows the effect of a second tiling.
Here blue is covered once, and red is covered twice. Blue peeks out
between the CCDs. On the right is the effect of three tilings, with 1
tile coverage in yellow, two tile coverage in green, and three tile
coverage in red[^4]. The distribution of coverage is textured.

The number of tilings has an effect on three areas, most obviously
exposure time. It also affects the image processing[^5] where voting
techniques allow robust rejection of artifacts on a pixel by pixel
basis, as long as there are 3 or more images that can vote, and the lack
of 3 images forces algorithms to consider neighboring pixels. This is an
example of where it is possible to keep complexity out of software by
survey design. The other area for which the number of tilings comes into
place is in calibrations.

Many or most of the systematics in photometric calibration are dependent
on the location in the camera. Multiple tilings provide leverage in
removing systematics by providing multiple exposures of the same piece
of sky through different parts of the camera. Tiling patterns that allow
for maximal overlap between adjacent tiles in different tilings provide
much more leverage than tiling patterns that, say, overlap the outer 5%
of the camera. And if the goal is to maximize unique overlaps, there
exists optimal mapping strategies. Arbitrarily take the first tiling as
the reference tiling (Figure 4.4a). The second and third tilings have
hex centers placed on the vertices of the reference tiling such that the
three centers form one of the 6 triangles that make up a hexagon (Figure
4.4b). They provide 33% overlaps with 3 hexagons from the reference
tiling. The 4^th^ through 6^th^ tilings have hex centers placed on the
centers of the edges of the reference tiling (Figure 4.4c). These
provide 42% overlaps with 2 reference tiling hexes, and 8% overlaps with
two others. This set of 6 tilings provides the maximally unique
interlocking pattern. The section on photometric calibration will
consider these effects further.

![](media/image39.png){width="3.575in" height="3.7416666666666667in"}4.
The statistics of the tiling coverage for the DES tile pattern. The
fraction of area covered N-1 times equals the fraction of area covered N
times for 5 tilings. For 3 tilings, 30% of the area is covered only
twice. As there are significant image processing simplifications and
algorithm improvements[^6] possible for three images, the statistics of
the coverage suggest 4 as the minimum number of tilings.

Figure 4.4 a) The left panel shows the reference tiling in black. b) The
middle panel shows the reference tiling and the two 33% overlap tilings
in red and blue. c) The right panel shows the reference tiling and the
three 42% overlap tilings in purple, green, and gold. These six tilings
provide a maximally unique interlocking tiling pattern.

### Survey Geometry and the Weather at Cerro Tololo 

![](media/image40.png){width="3.875in" height="3.4479166666666665in"}The
survey area is easiest to observe in Austral Spring. The season matters
because of the length of night and because of the weather. This can be
seen in Figure 4.4, where the longer nights of early spring are
balanced, on average, by the poorer spring weather, so that the actual
number of useful hours per night is the same in early spring (day \~225)
and early summer (day \~325).

Figure 4.5 Lunar cycles around the time of the survey. Plotted on the
left are the hours per night of dark time (thin curve) and photometric
dark hours per night (heavy squares). On the right are the average
fraction of cloudless time based on 30 years of weather data from CTIO;
the same data are in each of the right hand panels, modulated by full
moons and end of seasons. In the left hand panels, it is clear that the
length of nights may be longer in the early Austral Spring, but the
weather is better later in the early Austral Summer.

The calculation of the averages is possible because CTIO has kept useful
weather data over a 30 year span. The weather data statistics are
summarized in Figure 4.5, and both seasonal variations and the poor
conditions of the El Nino years can be seen. There is little that can be
done about the El Nino years: in any given El Nino year, one must endure
as little as half the photometric weather of a normal year, and
reasonable survey strategies must be robust against such a setback. On
the other hand, the seasonal patterns can be planned for. The high
summer days (numbers 0-70) have very good weather, the fall days
(70-135) exhibit rapidly decaying weather, and during winter and spring
days (135-365) the weather slowly gets better.

Combined, these data make the time span from September to January the
optimal time to pursue the observing of the survey area, as shown in
Figure 4.6. The footprint of our survey sets Austral Spring as the time
the survey is easiest to observe, but the Chilean weather patterns set
late spring and early summer as the times we are most likely to observe
the survey area successfully.

![](media/image41.png){width="4.25in"
height="3.7395833333333335in"}Figure 4.6 Thirty years of CTIO weather
data summarized as the average fraction of cloudless nights, plotted
against day of the year (top) and as a seasonal average/year (bottom).
In the top panel, the dotted line includes nights partly clear and
partly cloudy whereas the solid line is purely clear nights. In the
bottom panel, the triangles correspond to purely clear nights and the
squares to the dotted line in the top panel. Also in the bottom panel,
the heavy dotted lines show that these El Nino years have significantly
lower fractions of clear time. If a given year turns out to be El Nino,
one might endure a year with half the photometric time of a normal year.
(Note that the horizontal dashed lines in the top panel do not
correspond to the parameterizations in the text.)

This bodes well for the weak lensing component of our science goals, as
the seeing at the Blanco 4m gets better as spring moves into summer. The
site itself shows 0.85" seeing in October, 0.65" in November and
December, and 0.60" in January. Time in January is, for weak lensing
purposes, more valuable than time in August.

**An Analytic Calculation of the Time Available for the Survey:** Issues
of survey geometry and weather have been explored, but the question of
how much time is available for the survey remains, and so we turn to the
detailed analytic analysis presented in Wester (2004). The calculation
invokes specific criteria to address the issues of the spherical
astronomy of the survey area and the weather of the site.

The dark time is taken as the hours between the evening and morning
astronomical twilight, defined as the sun 18-degrees below the horizon,
during periods when no moon is above the horizon[^7]. Gray time is
defined similarly but allows for the presence of the moon and includes
18- to 12-degree twilight. We analyze the 5 contiguous lunar cycles that
end in January (143

![](media/image42.png){width="5.018055555555556in"
height="3.25in"}Figure 4.7 The black line shows the number of hours per
night that 2hr-stretches of survey RA are visible (airmass \< 1.5) as a
function of the day of the year. Important factors in the shape of the
black line are the RA range of the survey and the seasonal lengthening
and shortening of nights. The blue line shows the number of hours
multiplied by the fraction of clear nights using the seasonal
parameterizations given above. Obviously the 100 day stretch from
September through November where the average number of useful hours is
flat and independent of the length of the night.

nights). The survey starts on different dates in different survey years
in 2008-2012 due to the lunar cycle, which impacts the available time
since the time between astronomical twilight varies over the course of a
year.

To estimate the impact of weather, we analyze the 30 year CTIO weather
record, which quantifies the measured cloud cover during four quarters
of each night from 1975-2003. We count only photometric time, defined to
be when there is no cloud cover. While we have calculated the average
fraction of cloudless nights as a function of day of the year, we
instead use an analysis that better includes the effects of the El Nino
induced extreme conditions. This more sophisticated analysis maps the
historical weather statistics in all five-year periods from 1975-2003
onto our planned observing time in 2008-2012. This analysis is shown in
Figure 4.8.

We find average values of 1749 dark and 2142 gray hours over 5 years.
The five years cover 143 days, more than the 105 we will use for the
survey. We scale down to our expected survey usage by multiplying by
73%, resulting in

-   ![](media/image43.png){width="4.375in" height="3.125in"}1300 hours
    of useful dark time and

-   1600 hours of useful gray time and

-   an estimate of 2900 hours of total photometric time over the survey.

When a significantly illuminated moon is above the horizon, it is bright
time, but the light has the yellow spectrum of the sun and affects the g
and r bandpasses much more than the redder i and z bandpasses. How much
less affected is seen in Figure 4.8, which shows the distribution of sky
brightnesses of 1000 sq-degrees of SDSS imaging data, taken in dark
time, and of the effect of moon light on the sky brightnesses of SDSS
Photometric Telescope data as a function of separation from the moon. We
can work in any moonlight with the z-filter, and during most moon
conditions with the i-filter.

![](media/image43.png){width="4.25in" height="3.035416666666667in"}

![](media/image44.png){width="3.3819444444444446in"
height="2.7041666666666666in"}Figure 4.8 The total cloudless time over a
5-lunation period ending in January, over the last 30 years. The
methodology is to calculate the time available in 5 year moving windows.
The data points are therefore correlated, but then, so is the weather.

![](media/image45.jpeg){width="3.6458333333333335in" height="2.6875in"}

Figure 4.9 The plots of sky brightness. The left panel shows the
distribution of sky brightnesses for the 77,000 images in the SDSS Data
Release 1. The panel on the right shows the sky brightness as a function
of moon separation for images taken when the moon is at 50% illumination
or greater. These show that we can work in the i and z filters in full
moon conditions, in the z-band during any time, and the i-band when the
moon is 50 degrees or farther away.

There are also nights that are photometric but have poor seeing. If the
seeing is beyond the \< 1".1 window, set in the science requirements,
then the data we acquire will not be part of the survey coadd and we are
free to use it for other purposes. The CTIO weather records on seeing
are not as extensive as for clouds, but early estimates suggest 10% of
the observing time in the Austral Spring is worse than 1.1". Cloudy and
bad seeing data are not useful to us in the main though the criteria in
the supernova survey for useful data are less stringent, and we will
explore using the time for that. During periods of good seeing, time
neither photometric nor totally overcast, we will acquire data in our
normal fashion, but assign that data to a non-photometric tiling that we
can add into the survey coadd, but not use for survey calibration. As
there is less time in the non-photometric category, it is exceedingly
unlikely we would complete one non-photometric tile per year. Periods of
photometric and good seeing are prime survey time for i and z, and if it
is dark time, for g and r as well.

The photometric poor seeing time allows for an interesting use:
calibration of star fields in the survey area from standard stars. If we
devote this time to the transfer of the absolute calibration, we would
have roughly 300 hours to use for this purpose, as seen in Table 4.1, of
which approximately 130 hours are dark time. The \~1700 tiles in our
area would, if one out of 7 were used as a calibration field, require on
the order of 250 fields. Our rate of transfer star field acquisition
would have to be better than 2/hour, not a demanding rate. This idea is
developed further in the section on photometric calibration.

Table 4.1 Breakdown of Time over the 525 Nights of the Survey

  --------------------- --------------- ----------------- ----------------
  Hours                 Total Hours     Good Seeing (\<=  Bad Seeing (\>
                                        1.1")             1.1")

  Photometric           2900            2600              300

  Non-Photometric       950             850               100
  --------------------- --------------- ----------------- ----------------

  --------------------- ---------------- ----------------- ---------------
  Nights                Total Nights     Good Seeing       Bad Seeing

  Photometric           396              355               41

  Non-Photometric       129              116               13
  --------------------- ---------------- ----------------- ---------------

### Observing Strategies

The time to complete a tiling depends on observing strategy design
choices centering around exposure time and photometric calibration
strategy. The two design decisions we have made push us in a particular
way: that area is more important than depth drives imaging the entire
survey area at least once per year; and that the use of multiple tilings
enables precision photometric calibrations. The boundaries on the range
of reasonable strategies are not overly broad. We can examine the
implications of the remaining choices available by exploring two
strategies.

**Strategy A:** We divide the needed integration time into 100 second
exposures for the first five tilings, and 200 second exposures for any
remaining tilings. This choice leads to 5 tilings for g and r, 7 for i
and 13 for z. Five is chosen as the minimum number of tilings as a means
of reaching 1% relative photometry. At a given sky position images in
two filters (g,r in dark time, i,z in bright) are acquired before the
telescope slews to the next position. Positions are chosen along
constant airmass tracks in order to simplify the photometric calibration
equation, eliminating airmass as a term[^8]. This strategy aims at the
highest precision relative photometry.

![](media/image46.png){width="2.0416666666666665in"
height="2.4375in"}**Strategy B:** We aim at one tiling per year per
filter, and to take three years to do g,r and five years to do i,z. This
leads to 166 second exposures for g and r, 180 second exposures for i
and 420 second exposures for z. The data are acquired in a pattern
consisting of a main hex and its 6 neighboring hexes (Figure 4.9). Each
set of 7 hexes is taken preferably on the same night and airmass. At
each position, an image is taken through each of a pair of filters: g,r
if dark time, i,z if bright. Each main hex is the subject of a transfer
calibration from standard stars. The aim of this strategy is minimal
observing overhead combined with a more traditional photometric
calibration.

![](media/image47.png){width="4.0in"
height="2.3472222222222223in"}Figure 4.10 The spatial layout of the two
strategies. Strategy A aims at constant airmass stripes, which are shown
in the left panel as connected red lines for a series of constant
airmass observations taken in October. Strategy B aims at central hexes
(dark blue) and their zones of control, the 6 hexes immediately
surrounding the central hex and shaded light blue in the right panel.
Each strategy assumes that a single calibration along the stripe or in
the central hex will calibrate all of the connected hexes.

Table 4.2 Characteristics of Strategy A and Strategy B

  ----------- ------------ ----------- ---------------- -----------------
              g tilings    r tilings   i tilings        z tilings

  Strategy A  5x 100 sec   5x 100 sec  7 (5x100, 2x200) 13 (5x100, 8x200)

  Strategy B  3x 166 sec   3x 166 sec  5x 180 sec       5x 420 sec
  ----------- ------------ ----------- ---------------- -----------------

  ------------------------- ------------------------- -------------------
  Number of tilings         Strategy A                Strategy B

  Year 1                    g,r,iz = 2,2,2,2          g,r,iz = 1,1,1,1

  Year 2                    g,r,iz = 4,4,4,4          g,r,iz = 2,2,2,2

  Year 3                    g,r,iz = 5,5,6,6          g,r,iz = 3,3,3,3

  Year 4                    g,r,iz = 5,5,7,9          g,r,iz = 3,3,4,4

  Year 5                    g,r,iz = 5,5,7,13         g,r,iz = 3,3,5,5
  ------------------------- ------------------------- -------------------

Each strategy employs a telescope camera system that is capable of 35
second slews, 15 second readouts, and 10 second filter changes, any of
which can be simultaneous.

The survey observing time is divided into another two categories: the
main imaging time and the supernova survey. Ten percent of the available
time is devoted to the supernova time domain survey. Further, perhaps
five percent of the time the system will be down for repair or upgrade.

Doing the arithmetic, one finds that strategy B obtains 1 tiling per
year and per filter[^9] and that strategy A obtains 2.2 tilings per year
per filter. Given the 35 second slew time, aiming at significantly more
than 8 tilings per year total would be inefficient and would have to be
driven by some compelling need. Some characteristics of the tilings
obtained as a function of time are shown in Table 4.2. There is one
number that makes it easier to do quick calculations: 800 seconds/year.
It is possible to cover the full 5000 sq-degrees with a 3 sq-degree
camera in 30% of a year with 800 seconds of exposure: 8 tilings of 100
seconds exposures or 1 tiling of 800 second, the difference in overhead
is about 20%, so for quick calculations can be neglected.

Well planned large homogeneous surveys can obtain all of their data at
low airmass: both strategies place a limit on observations of aimasses
\<= 1.5. There is some room to make this airmass limit smaller.

In both strategies, the spatial layout of observations is driven by
plans for photometric calibration. The connected observations allow one
to transfer sparse photometric calibration images onto a larger number
of images, 7 for strategy B and roughly 13 for strategy A. The spatial
patterns are shown in Figure 4.10.

Table 4.3 Characteristics of Strategy A and Strategy B

  ----------- ------------ ----------- ---------------- -----------------
              g tilings    r tilings   i tilings        z tilings

  Strategy A  5x 100 sec   5x 100 sec  7 (5x100, 2x200) 13 (5x100, 8x200)

  Strategy B  3x 166 sec   3x 166 sec  5x 180 sec       5x 420 sec
  ----------- ------------ ----------- ---------------- -----------------

  ------------------------- ------------------------- -------------------
  Number of tilings         Strategy A                Strategy B

  Year 1                    g,r,iz = 2,2,2,2          g,r,iz = 1,1,1,1

  Year 2                    g,r,iz = 4,4,4,4          g,r,iz = 2,2,2,2

  Year 3                    g,r,iz = 5,5,6,6          g,r,iz = 3,3,3,3

  Year 4                    g,r,iz = 5,5,7,9          g,r,iz = 3,3,4,4

  Year 5                    g,r,iz = 5,5,7,13         g,r,iz = 3,3,5,5
  ------------------------- ------------------------- -------------------

### Simulating the Observing

The survey observing strategies can be tested against simulations of the
survey progress if the simulations incorporate the major elements of
survey observing, such as the survey geometry and the weather. In the
pursuit of an optimal survey observing strategy we have constructed such
an observing simulator.

For our simulator, we took the night as the base element and incremented
time from sunset astronomical twilight to sunrise astronomical twilight.
The night was noted as "moony" if the moon was at 50% illumination or
greater. The nights allocated to NOAO community observing were assigned
the earliest possible dates in the month. The weather was checked, and
the night declared photometric or cloudy. If non-NOAO, dark time, and
clear, the observing commences at astronomical twilight with an
observation (two, actually, as two filters are observed at the same
position) and a slew. Time is incremented, and data obtained until the
sunrise astronomical twilight. The observations are selected by looking
at the pool of unobserved tiles in a tiling at less than 1.5 airmasses,
and then picking the most interesting tile in ways idiosyncratic to the
particular observing strategy.

This structure allows the major elements of the survey to be assigned
models:

-   A NOAO time allocation model, in which NOAO reserves

    -   September: 4 bright /4 dark nights

    -   October: 4 bright /5 dark nights

    -   November: 4 bright /4 dark nights

    -   December: 4 bright /4 dark nights, with the telescope shut down
        Dec 25 and Dec 31.

    -   January: 4 bright /5 dark nights, 2^nd^ half of remaining nights

    -   February: 3 bright /3 dark nights, 2^nd^ half of remaining
        nights

    -   The remainder of the time in September through February devoted
        to the Dark Energy Survey, a total of 108 nights/year.

-   A weather model:

    -   Based on the 30 year CTIO weather data

    -   The percentage of clear nights (F) given a day number (d):

F = 70 + 0.00\*d 0 \< d \< 70 Austral Summer

F = 124 + -0.77\*d 70 \<= d \< 135 Austral Fall

F = 0 + 0.17\*d 135 \<= d \<= 365 Austral Winter/Spring

-   The footprint of the survey:

    -   SPT

        -   -60 to 105 degrees RA, -30 to --65 Dec

        -   -75 to --60 degrees RA, -45 to --65 Dec

    -   SDSS Stripe 82

        -   -50 to 50 degrees RA, -1.0 to 1.0 Dec

    -   Connecting region

        -   20 to 50 degrees RA, -30 to --1.0 Dec

**Strategy Evaluations:** Our metrics are driven by the science, and
are:

-   Efficiently attain required depth

-   Minimize photometric calibration errors

-   Obtain scientifically interesting data at natural points in the
    survey

    -   Year 2, at the time of the coadded data release

    -   Year 5, at the end of the survey

For our purposes here we will simplify the footprint to the rectangular
region that can be seen in Figure 4.10. The simulation was run here for
2008. Strategy B was simplified from zone of control around a central
master hex to a line of 6 hexes around the central master hex, a
simplification which will have no effect on our comparison. Strategy A
nearly completed the tiling for both tiles; recall that Strategy A aims
at two tilings per filter in the first year. Strategy B appears to not
have completed its single tile, but this is an artifact of the strategy
model. Strategy A attempts to start at the farthest west, lowest
declination unobserved hex, whereas Strategy B attempts to start at the
farthest west unobserved master hex. That master hex starting point is
significant: if the 7 hex line is not completely observed, the
implemented model has no means of finishing off the observations. Time
to do so was available, however, so we assume that Strategy B would be
able to finish the tiling with point and shoots.

![](media/image48.png){width="3.441666666666667in"
height="1.9284722222222221in"}Figure 4.11 Sample tiles from Strategy A,
on the left, and Strategy B, on the right. The strategy for B was
simplified to be 7 hex long lines centered on the master hexes, a
simplification that will not affect our comparisons. The unobserved
tiles are shown as black dots. In Strategy A these were legitimately
missed, whereas in Strategy B they resulted from a strategy model that
did not allow for the finishing of the 7 hex strings when airmass or
dawn interfered. There was plenty of time left in Strategy B to pick
these up; we assume that it completed the tiling, and added them in to
the statistics where appropriate.

**Efficiently attain required depth**: The distributions of image
airmasses is shown in Figure 4.11. In both cases the majority of the
observations are at airmass \< 1.25. Strategy B has a noticeably larger
tail of high airmass observations, and it is likely that the inclusion
of the fill in hexes will increase this. The statistics however, are
very similar: mean airmass = 1.20, and median airmass = 1.17.

![](media/image50.png){width="3.4791666666666665in"
height="2.78125in"}Figure 4.12 The distribution of airmasses. All
observations are at airmass \< 1.5, and the majority of both strategies
are at airmass \< 1.25. Strategy A is the solid line, and Strategy B is
the dotted line.

The distribution of slew lengths shows some differences. The mode of the
slew distribution for Strategy A is 1.6 degrees, and the mode for
Strategy B is 2.0 degrees. The mean, median, rms lengths of the constant
airmass stripes in Strategy A are: 17, 13, 16, whereas the mean, median,
rms lengths of the zone of control stripes in Strategy B are: 6, 7, 1.5.
The lengths are noticeably smaller in Strategy B, and this is before the
14% of all tiles in the "broken hexes" that must be slewed to are
included. Likewise, the 75% quartile of the Strategy A slew distribution
is 2 degrees, where in Strategy B, again without the broken hexes, is 5
degrees. The telescope does, in fact, handle these 5 degree slews with
the same level of efficiency as it does for 2 degree slews. Data on slew
times from the CTIO 4m can be described by the following model:

Table 4.4 CTIO 4m Slew Times

  ---------------------------------- ------------------------------------
  Slew (degrees)                     Time (seconds)

  \<= 3                              34

  3 \< slew \< 20                    2\*slew + 27

  \>= 20                             75
  ---------------------------------- ------------------------------------

The distribution of slews (with Strategy B's broken hexes assigned a 10
degree slew) along with the model of the slew times and our expected
readout time of 17 seconds allows the calculation of an overall
efficiency. We calculate the inefficiency over all 4 filters and all
five years. For Strategy B it is 14%, and for Strategy A, 20%.

It is worth calculating what improvements in the telescope slew
performance and camera readout times would gain us. If the telescope
control system could be improved to give 17 second slews for 1 degree or
less slews, with the slew time rising smoothly to 34 seconds for 3
degree slews, the inefficiency of Strategy B improves to 13% and
Strategy A to 14%.

The increase in efficiency would translate directly into longer exposure
times and deeper overall data set. It should be pointed out that this is
true for Strategy B in comparison to Strategy A as well.

**Minimize photometric calibration errors:** The biggest difference
between Strategy A and Strategy B is in the number of tilings obtained
per year, and the resultant photometric calibration accuracies. The
calibration accuracies depend on the number of tilings N as sqrt(N) for
the absolute calibration[^10], essentially from calibrating the same
piece of sky every year. For relative calibrations, the accuracy
increases better than sqrt(N), but not as well as N. It is clear that
having more tilings will produce better photometric calibrations.
Whether 3,4, or 5 tilings is sufficient to reach the survey goals
depends in large part on the (as yet unknown) level of systematic errors
in the Dark Energy Survey instrument.

Table 4.5 The expected calibration accuracy

  ------------------- ---------------------------- -----------------------
                      Absolute: B/A                Relative: B/A

  Year 2              g,r,i,z = √2                 g,r,i,z = 1.5

  Year 5              g,r,i,z = √5/3, √5/3, √7/5,  g,r,i,z =
                      √13/5                        1.5,1.5,\~1,\~1.5
  ------------------- ---------------------------- -----------------------

**Obtain scientifically interesting data at natural points in the
survey:** The interesting points in the survey are at 2 years and 5
years; the interesting metric is the quality of the data set at these
times. We are to eplore the time progression of the survey.

![](media/image51.png){width="3.75in" height="1.8409722222222222in"}For
this, we will use the full footprint, which is shown in Figure 4.13.
This footprint has been changed to make the observing easier. The
changes: the lower border of the area is --65 degrees declination: lower
declinations are hard to observe and include the LMC. It was replaced
with the area in the west. The connecting region was thinned to 30
degrees width, and moved as far to the east as possible before the
galactic dust distribution contributes substantial extinction.

Figure 4.13 Simulations of the progression of observing in 2009. On the
left is a great year, on the right is a year whose weather is worse than
normal.

The plots in Figure 4.13 show the effects of weather, especially in the
patterns of untiled sky on the right panel. The distribution of number
of tiles observed versus hours of clear weather is interesting. As can
be seen in Figure 4.14, we image the entire survey area in about 1/3^rd^
of the simulations, even if we have an optimal hex selection algorithm.
There remains a 10% discrepancy between the analytic model of the
weather and the simulation

which we have not tracked down; the analytic model predicts 10% more
time The figure also shows that bad years are bad years; there will be
years in which the weather is poor, and we will achieve less than full
coverage on the tilings.

![](media/image53.png){width="4.0in" height="2.625in"}

Figure 4.14 The distribution of tiles observed versus hours of dark
clear nights. The footprint has 1750 tiles [^11]. All of these
simulations were for 2009. The red symbols shows a variation on the time
distribution in which survey nights in late February are traded for
nights in December. The curve is essentially linear to about 250 hours,
and then flattens out. The flattening is due to time available that
cannot be used in the tiling; in other words the hex selection algorithm
can be improved. Two thirds of the simulations are in the linear regime:
2/3rds of the time we will observe 1667 tiles. The change in time
distribution did little.

The distribution of time allocated to the survey may be used to shape
which part of the survey is most likely to be missed due to weather in a
given year. We ran simulations that allocated time from the half nights
in the 2^nd^ half of February to full nights in December. Once the
simulations are averaged, the probability map may be examined. Two of
these are shown in Figure 4.14. Similar in success rate, the diagrams
are dissimilar in spatial patterns. Given less time in December and more
in February, the standard time distribution is less able to fill in the
connecting region between the SPT and the SDSS Stripe 82 area, but more
able to complete the SPT area in the far east. Both suffer from a lack
of time in October.

The spatial probability pattern of the standard time allocation is
favorable[^12] to the science priority of the three regions. The SPT
area is of prime importance, the SDSS Stripe 82 is of great interest for
calibration of photometric redshifts, and the connecting area's
strategic usefulness rests on its direct connection of the photometric
redshift training ground to the main survey area. The three pillars in
the submarine conning tower of the left panel of Figure 4.14 provide the
minimum necessary to do this, though the hex selection algorithm would
have to be guided to their importance.

![](media/image54.png){width="3.375in" height="1.6444444444444444in"}

Figure 4.15 Probability maps of the survey area. Dark corresponds to the
highest density of observations of a hex, light to the lowest. The panel
on the left shows the reference distribution of survey time. The panel
on the right shows what happens if the time in the second half of
February is instead given to December. The probability map is more
uniform, but the coverage of the main SPT area is lower. The reference
distribution shows a fairly uniform distribution in the SPT area (modulo
problems on the left, a lack of time in October), with the connecting
area not as well imaged.

The simulation is lacking several features, of which photometric nights
with poor seeing and the supernova field observations are both 5-10%
effects, that perhaps balance the difference in time that is clear in
the simulations versus the analytic calculations.

### Reference Strategy

We will take the lessons learned from the simulation and develop the
reference design survey strategy.

First, the sparse tile pushes us to four or five tilings in order to
have three or more observations everywhere. Our goal of science quality
data at the end of the second year then demands multiple tilings per
year per filter. Second, the overhead of thirty five seconds per slew
pushes us towards longer exposures, ideally 350 seconds (or lowering the
slew time to 15 seconds). This pushes us back away towards as few
tilings as possible, down to two per year. Third, the realities of
weather can cut the clear observing time in half. A strategy aimed at
two tilings per year can still complete a tiling in the face of bad
weather.

A good reference strategy is to steer the middle course between strategy
A and strategy B, aiming to get two tilings per year per bandpass. We
would obtain g and r data for the first two years, i data for the first
4 years, and z data for all five years. Details of the reference
strategy are shown in Table 4.6. Increasing the exposure time above 100
seconds decreases the overhead and thus there is more time available for
on-target integration, but we have not included this in the total
integration times. In year 5, for example, effectively another tiling of
200 seconds should be possible.

Table 4.6 Reference Survey Strategy ![](media/image1.wmf)

The reference design presented here will obtain 4 tilings of data at the
end of year two (3 if there is a year of bad weather), sufficient to
have 3 exposures per point for over 90% of the survey area. It will
produce a dataset capable of being calibrated to \< 2% relative and \<
3% absolute accuracy. The reference design is flexible enough to deal
with an El Nino year at any point. The price will be less depth in the z
band. In fact, a careful examination of the science requirements versus
the reference design expectations will note that we meet theour
requirements at the end of year 4. We take the year 5 data in z in order
to increase the depth and therefore the weak lensing grasp, but weather
is a problem. In a five year period we should expect an El Nino so the
conservative survey designer will also take the year 5 times as a buffer
and contingency to ensure meeting the requirements.

In this conservative view, in the unlikely event that we experience 5
good years of observing, without an El Nino event, the resulting excess
contingency time can effectively be used in a number of ways. It could
be used to acquire more z band data, as envisioned in the reference
design, to increase the weak lensing grasp. It could be used in part to
target the SPT "blank fields", those SPT sources without DES
counterparts, in J and H using NEWFIRM as a means to collect the small
percentage of SPT clusters at z \> 1.5. We will continue to develop
ideas for the good luck case while we continue to retain the fifth year
contingency in our design.

## Calibration Strategy (WBS 1.3.2)

There are three components covered in calibration: photometric
calibration, astrometric calibration, and photometric redshift
calibration.

### Photometric Calibration

We are proposing a large area, multicolor, imaging survey requiring
precision photometry. Traditional photometric reductions which place
observations onto a standard system are inappropriate here. It is much
more important for extremely large sets of homogeneous photometric data
produced using a single instrument to be internally consistent than it
is for them to be on previously established photometric systems. For
such surveys, the aim of calibration is to produce data sets for which:

-   The magnitudes may be calculated by convolving a spectrum with good
    spectrophotometry with the system bandpasses, and

-   The magnitudes vary only by 2.5log~10~(), where is the ratio of the
    photon fluxes, independent of position.

-   The magnitudes have a well-defined absolute zeropoint.

The first goal puts a premium on knowing the system response curve with
precision. The second goal demands knowing the atmospheric effects, but
also puts a premium on understanding flat fielding and scattered light
effects. The third is a question of standard star observations. We will
discuss these three requirements in the next three sections.

A multiply imaged survey demands a calibration strategy that differs
from both a single pass survey and a deep pencil beam survey. The pencil
beam surveys proceed by observing a set of standard stars at a variety
of airmasses in order to calculate the parameters of an atmosphere
model, from which one can calibrate data taken anywhere in the sky. This
form of precision calibrations demands a concordant amount of the
telescope time be devoted to the standard stars. Single pass surveys
cannot afford the devoted time, so they employ a dedicated small
telescope to monitor extinction during a night and to transfer a star
network down to fainter magnitudes to calibrate the main survey. In a
multiply imaged survey one should take advantage of the multiple images
by acquiring them on different nights and thus averaging down the
absolute calibration error. One should also acquire the images on offset
pointings of the scale of the camera to average down the relative
calibration errors.

**Determining the System Response:** The observed magnitudes must be
predicted within 2% by convolving calibrated spectra with the system
response curves. *We must measure the system response as a function of
wavelength.* Understanding the system response curves is critical to
using photometric redshifts, and is in fact fundamental to understanding
the photometric data.

System response curves are relative efficiency curves over the entire
wavelength coverage of the instrument. The measurement of system
response curves requires shining calibrated monochromatic light through
the corrector, filters, and onto the CCDs. The light must be sent at the
correct f-ratio, because the angle the light beam makes to the surface
changes the wavelength response of interference coatings. The response
curves must be measured using on the order of 100 resolution elements
across each bandpass, which for our 150nm wide filters is 1.5 nm
resolution.

We plan on doing this at the telescope with a system that will perhaps
be more capable than what we describe here, depending on the outcome of
a research program CTIO is pursuing. Regardless, we plan on measuring
the response curves before we deliver the camera, during the full up
testing of the camera at Fermilab. The system will require creating test
beam optics that create a f/3 beam to be sent to the focal plane array,
though we plan on illuminating only one CCD at a time.

This system will also be able to test the other components of an
astronomical photometric instrument: flat fields and read noise being
the two most important. Testing of the entire Dark Energy Camera system,
from focal plane to corrector to data acquisition to observing programs,
is of course a very important task. It should be done before we ship the
system to CTIO.

**Relative Photometry:** Relative calibration is the step of placing all
of the instrumental magnitudes, onto a magnitude system corrected for
instrumental effects and the atmosphere, but without regard to the
magnitude zeropoint.

Overlapping tilings can make this a powerful technique, and we will use
them to perform high precision relative photometry. The essential step
is to use ratios of the counts of the same stars observed on two
different images to tie the photometry together. Large scale overlaps
provide stars appearing in two different images providing numerical
precision on the ratios better than 1%, much better than one can know
the zeropoints of the images. The limiting factor in this approach is
systematic errors, primarily flat fielding and scattered light. Both of
these tend to be more problematic at the edges of the field of view. The
overlapping hex tilings allow one to average these out. The relative
photometry strategy produces a very flat map, optimal for relative
photometry.

Given these overlapping tilings, the optimal way to think about relative
photometry is to ask for the best mapping of the underlying star field
given the observations of the star field. The techniques developed for
CMB mapping strategies (Wright 1996, Tegmark 1997) can be applied to
this problem. The mapping strategies are in essence least squares
solutions to the problem of estimating the underlying map given many
noisy observations. One forms linear equations of the form
***y***=***Ax***+***n***, where ***y*** is the data, ***n*** the noise,
***A*** is the known observation matrix and ***x*** is the underlying
map one is solving for. One solves for the map ***x*** =***Wy***, by
constructing ***W*** via This method is fast, simple, and minimizes the
error map variance in the case of white noise.

We have explored this technique using a model for the systematics of the
camera.

-   A multiplicative flat field gradient of amplitude 3% from east to
    west.

-   A multiplicative 3% rms flat field error per CCD

-   An additive scattered light pattern with a amplitude from the
    optical axis, 3% at the edge of the camera

-   An additive 3% rms scattered light per CCD

The amplitude of these effects are probably slightly worse than the
DECam will exhibit, and work towards understanding the actual levels is
ongoing. We also incorporate an atmospheric model for wavelength
dependent fluctuations in the extinction at the level we expect at CTIO,
with the additional feature of a 10% gradient in the extinction value
during a night, which would induce large scale RA dependent zeropoint
variations. We simulate the progress of the survey with the survey
simulation program mentioned briefly last section, and the relative
mapmaking algorithm. The decrease in the systematic error floor level is
faster than sqrt(N) but slower than linear in N, the number of tilings.
A table showing the improvement is below, and a realization of a
resulting map is given Figure 4.15. This process gives a very flat map,
receptive to an absolute calibration.

  ----------------------------------- -----------------------------------
  Relative Calibration                

  Tiling                              σ

  1                                   0.035

  2                                   0.018

  5                                   0.010
  ----------------------------------- -----------------------------------

![](media/image2.png){width="3.152755905511811in"
height="1.7728346456692914in"}![](media/image3.png){width="2.2625984251968503in"
height="0.1279527559055118in"}.

Figure 4.16 This figure shows a map of the photometric calibration error
using relative photometry. The full scale of the scaling bar is --0.20
mags to +0.20 mags. The map is that resulting after 3 tilings and has a
rms scatter of σ=0.013, starting from 10% photometry and a variety of
flatfield and scattered light systematics imbedded at the σ=0.03 level.

**Absolute Calibration:** Absolute calibration is the process of
transferring the calibration from known standard stars onto unknown
stars. The calibration of standard stars includes an energy flux
calibration, and the process of absolute calibration determines the
ratios of the energy flux between the unknowns and the standards.

There are choices to be made here in the pattern of observations. We
consider two scenarios. The first is motivated by a desire to convert
into a far simpler form the standard equation of photometry, *m* = -2.5
log*C* - *a*- *k*(*t*)*X* - *c*∆*M*∆*X* where *m* is the magnitude of an
object, *C* is the measured counts, *a* is the measured zeropoint,
*k*(*t*) is the time dependent extinction coefficient, *X* is the
airmass, *c* is the second-order extinction coefficient. The last term
reflects the varying extinction across the broad filters typical of
large area surveys. First, one notes that wide area surveys have a limit
on the maximum airmass they will accept: airmass 1.5 is a pragmatic
choice. This eliminates the c term, as the values of *c* are of order
0.03 and thus the second-order extinction term is of order 0.005.
Ignoring it places a photometry calibration error floor somewhat below
1%. Second, one notes that the zeropoint *a* changes night to night,
while the extinction coefficient *k*(*t*) changes both night to night
and drifts during a night. If one were to arrange to observe only at one
airmass during the night, the equation would become *m* = -2.5 log*C* -
*a*(*t*). We thus aim for long strips of hexes all observed at the same
airmass. Later a hex along the constant airmass strip is absolutely
calibrated, and the rest of the hex assumed to be on the same
calibration.

The second scenario aims at providing absolute calibrations to compact
areas. The observation progression is to observe a hex and then its
"zone of control", the 6 surrounding hexes, before moving on to a new
central hex. Later the central hex is absolutely calibrated and the zone
of control hexes is assumed tied to it.

An absolute calibration, albeit to low accuracy, is possible as soon as
the standard stars are taken. For one tiling, the experience is that
absolute calibration is possible to 3-4% with the roughly ten standard
star observations that current survey techniques use.

After multiple tilings are in hand, one can improve the accuracy of the
absolute calibration by using the fact that we observe the same fields
on different nights, using different observations of the standard stars.
We use the same atmospheric model that we described above but here taken
to have 5% uncertainty, the constant airmass observation progression,
and neglected systematic errors. We solved for the zeropoint map using
the coadd technique described above. We find that one determines the
zeropoint of the coadded images with precision, where N is the number of
tilings in that filter. Starting at 5% rms zeropoint error, one reaches
2% after 5 years by leveraging the many observations of standards that
are taken. This 2% is the RMS constraint on any gradients or small scale
features in the map. We show a map of photometry errors resulting from
this approach in Figure 4.17.

In both scenarios, we plan to use the photometric time with seeing
quality not acceptable for the imaging survey to transfer the
calibration from standard stars to \~ 10 to 20% of the hexes. These
exposures would be shorter than an imaging survey exposure because of
brightness at the standards, so they should fit in an available poor
seeing time.

There are two calibration programs ongoing that will be helpful to the
DES calibration effort. The NOAO Surveys Project of Smith, Tucker and
collaborators is constructing a Southern u',g',r',i',z' standard star
grid. They use the CTIO 0.9m telescope to observe stars between 8 \< r'
\< 18 magnitude in fields spaced every hour in RA at Declination --30,
every two hours at Declination --60, every four hours at --75, and in
several special fields. The program is fairly far along and will be
completed by the time of the DES.

These stars are very good standards for the DES, as can be seen in
Figure 4.18. Recall that we will not be aiming to place our observation
directly on the u',g',r',i',z' system (the SDSS is not, for example),
but onto the natural instrumental system of the DECam.

![](media/image4.png){width="3.205511811023622in"
height="1.7980314960629922in"}![](media/image3.png){width="2.2618110236220472in"
height="0.1279527559055118in"}

Figure 4.17 This figure shows a map of the absolute photometric
calibration error using map making techniques. The full scale of the
scaling bar is --0.20 mags to +0.20 mags. The map is that resulting
after 3 tilings and has a rms scatter of σ=0.037, starting from 10%
photometry. The level scales with initial photometry level and with
sqrt(N) of the number of tilings: for 5% photometry and three tilings
the rms scatter would be σ=0.022.

The second program is the Stromlo Southern Sky Survey (SSSS), a project
led by Dr. Brian Schmidt of the Australian National University. This
five year project will provide a precision calibration of the whole
southern sky in the photometric bands ugriz+stromgren_v to depths 0.4
magnitudes deeper than the SDSS. For our purposes, this data set would
at least replace the USNO-B catalog as a source of per image
calibrations, and has the potential to be the "standard star network" of
choice.

Lastly, we expect that community users will observe standards especially
for non-standard filters such as DDO51 and the narrow band interference
filters. Although not specifically useful for the survey calibration,
this could be helpful for other users of the DECam.

![](media/image5.jpeg){width="2.40625in" height="3.0in"}

Figure 4.18 Standard Stars from the SDSS Southern Standards Program. On
the left is the spatial distribution of standard stars in the program.
On the right is a plot of the error distribution versus magnitudes of
the standard stars in one of the standard fields, which serves to show
us the magnitude range of the standards.

**Checks on the Photometry:** We must be able to check the accuracy and
precision of our calibration.

The mapping strategies we have explored aim to measure magnitudes, and
make no use of colors. This allows us to use known astrophysics to check
our photometry. The stellar locus in color-color space has extremely
well defined principal axes. The SDSS has shown that in the twin color
spaces of g-r, r-i and r-i, i-z one knows the location and direction of
the principal axes to a precision of

where is of order 0.003 magnitudes. In single exposures of 100s, the DEC
reaches about 21 mag at a S/N=100. This implies about 2000
stars/sq-degree. The SDSS experience is that only about 1/4 of these are
clean, in the sense of being located away from image edges and not
blended with other objects. We will have about 500/sq-degree of useful
stars, which corresponds to roughly 20/CCD. This means we can use the
stellar locus in three ways to check our calibrations:

-   CCD calibration on full sensor scales: Three adjacent hexes provide
    enough stars to check the calibration on each CCD to 0.003. Recall
    that the equation above scales to 64 bins, and our current designs
    have about 60 CCDs.

-   CCD calibration in 100x100 pixel scales: A complete tiling of 2000
    hexes provides enough stars that when one selects all the stars
    observed by a given CCD one has enough statistics to explore the
    calibration in 100x100 pixel sub-regions.

```{=html}
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-   CCD calibration on 300x300 pixel scales: Breaking the complete
    tiling up into 10 blocks of 200 hexes each, one can do the same on
    300x300 pixel sub-regions and check for variations with, say, right
    ascension.

It is worth pointing out how powerful this check is. We are calibrating
the magnitudes independently of the colors, and checking the resulting
colors, which are magnitude differences, using a precise test.

### Astrometric Calibration

The requirement on astrometric calibration is \< 0.1" absolute. The
design of the corrector optics provides for low, but non-zero,
distortion. It may be necessary to include terms beyond the standard
linear (affine) terms in the World Coordinate System convention. IPAC
has put forward an extension to account for the distortion in the
Spitzer Telescope IRAC instrument, and we will adopt this convention.

Astrometric calibration of images from DECam is straight forward and
will be implemented using a variant of the astrometric pipeline
developed for the Red-Sequence Cluster Survey (RCS). This method has
been demonstrated to work well on similar mosaic data from the CFH12K
(on the CFHT), Mosaic-II (on the CTIO 4m) and the currently largest
operating imaging mosaic, MegaCam on the CFHT. Astrometric calibration
will be done in a two-stage process. First, an astrometric reference
frame of a sufficiently densly populated field will be used to define a
relative solution between all chips in the camera. Each chip is treated
by a low order polynamial (typically 2nd-3^rd^ order) which maps pixel x
and y in relative RA and DEC about some fiducial camera center. The
reference field will be chosen to provide a density of 500+ objects per
chip to make this mapping precise and robust. This reference field can
be a moderate galactic latitude field with existing USNO-B astrometry,
or more preferably one of the equatorial fields with precision
astrometry developed as reference fields for the Sloan Survey. Snapshot
reference fields can be acquired in twilight on a regular basis
(typically a few times a run) to monitor the camera\'s astrometric
stability. The full image for all chips in DECam for a given science
pointing is then mapped onto the sky as defined by the USNO-B catalog
using a second polynomial which spans all chips. Over the area of DECam
there will typically be 1000\'s of objects to match to, which is more
than sufficient to ensure a good solution. Application of this algorithm
produces astrometry which is accurate to about 0.1\" in each coordinate,
as is shown in Figure 4.35 for existing RCS mosaic data.

![](media/image7.png){width="4.427083333333333in"
height="3.5416666666666665in"}

Figure 4.19 The histogram shows the distribution of residuals (both
coordinates summed in quadrature) between RCS and SDSS positions for
moderately-bright point sources in the overlapping fields. This
comparison independently shows that the typical residual, per
coordinate, is about 140 milliarcseconds between the SDSS and RCS. The
uncertainty, per coordinate, for the SDSS is 50-100 milliarcseconds,
suggesting that the RCS (and by extension, the DECam data) can be
astrometrically calibrated to a similar accuracy.

### Photometric Redshift Calibration

At the core of all of the science goals is accurate photometric
redshifts. Our plan is to observe the areas of several ongoing redshift
surveys. We expect to require roughly 1000 redshifts per 0.1 bin in
redshift from 0.1 \<= z \<= 1.5. This requirement becomes progressively
more difficult as the redshift is increased. We plan to overlap several
surveys. These include the 200 sq-degree SDSS Southern Survey, which has
100,000 redshifts down to i=20 and out to z=0.5, in combination with the
2df-SDSS survey, which has 10,000 redshifts of red galaxies out to
z=0.75. In addition, two other large but deeper redshift surveys, the
VIMOS VLT Deep Survey and the Keck DEEP2 Survey, are both currently in
progress and will overlap the DES on or near the equator. These surveys
will provide of order 100,000 redshifts, out to z=1.5 and above, useful
for photometric redshift calibration of the DES.

In Section 2.7, we described our initial simulations of photometric
redshifts for red cluster galaxies as well as for the general galaxy
population. Our current plans for improving our understanding of
photometric redshift calibrations will focus on a detailed
characterization of the photo-z error distribution (i.e., mean,
variance, skewness, catastrophic error rate) for galaxies down to the
DES depth (i = 24). This will be done as a function of the most relevant
galaxy parameters, in particular magnitude, redshift, and SED type or
color. For this purpose, we are using mainly data from the GOODS HDF-N
and CDF-S fields, which contain spectroscopic galaxy redshifts to the
relevant DES depths, along with available multicolor imaging. We will
use the GOODS data sets, as well as simulated Monte Carlo galaxy
catalogs generated from them, to characterize the photo-z errors
expected for the DES observational parameters. In addition, we will
supplement the GOODS sample with CNOC2 and SDSS data, which do not reach
the DES imaging depth, but will provide, at brighter magnitudes, much
larger spectroscopic redshift samples than are possible from the smaller
GOODS fields

## Survey Data Simulation (WBS 1.3.3)

The survey has precision measurements of dark energy as its highest
goal. To extract our science, we must understand both the cosmological
physics we are using in the measurements and the impact of the
instrumental signatures on those measurements. To design our survey, we
must understand what we would expect to observe under various scenarios,
be it the question of simplicity of selection function with redshift for
clusters given a range of i or z limiting magnitudes, or the effect on
weak lensing moment measurement efficiency of decreasing the point
spread function by 0.1". *We must produce a scientifically reasonable
simulation of the survey.*

We envision using a combination of analytic methods, catalog-level
simulations and image-level simulations for the Dark Energy Survey.
Analytic models and methods (e.g. Fisher analysis) provide us with a
quick means of estimating measurement and parameter uncertainties.
Catalog-level simulations provide the next step of complexity, and allow
us to incorporate more detailed and realistic models of statistical and
systematic errors (on e.g., photometry, shear, photo-z's), and also
allow us to test and optimize the science analysis pipelines using large
mock data sets. Finally, full image simulations provide a means of
characterizing instrumental or observational systematic effects which
are otherwise difficult to model analytically ab initio. An example
would be measuring flatfielding or scattered light photometric
residuals, as the images are propagated through the many steps of the
coaddition and data reduction pipelines. Such image simulations then
provide the data needed to analytically model the systematic effects and
to subsequently feed them back into the catalog simulations in a simple
manner.

One aim is to build up simple fitting functions and approximations from
the simulations that can be applied in the analytical techniques.

### Catalog Level Simulations

The start for this effort is in simulations of the dark matter
distribution either from N-body simulations or from analytical
approximations. Work will be needed here, probably from outside the
collaboration, to extend these simulations to a grid in and *w*.
Collaborator Albert Stebbins (Fermilab) is exploring a fast analytic
technique using mult-gaussians to approximate the dark matter
distribution.

Next, galaxies must be placed on the dark matter distribution. The
spectral energy distributions (SEDs) of the galaxies must be assigned,
and apparent magnitudes calculated. The output of this is a catalog
containing ra, dec ,z, mag, SED for the galaxies in the survey area,
along with catalogs of the dark matter clusters and the expected shears.
The current simulations that do this do not deal with size, shape or
morphology, and they will have to be extended to incorporate these,
likewise for the effects of galactic dust. Further, the galaxies that
are put in usually are limited to brighter galaxies due to the
resolution of the N-body simulation, and an analytical model is needed
to put in the lower luminosity galaxies. The output of this work is a
galaxy catalog. Collaborator Risa Wechsler is working on accurate
methods of placing the observed galaxy distributions onto N-body
simulations. We are also pursuing analytical expressions for observed
galaxy property distribution functions and photometric redshift error
distributions, using relevant imaging and redshift data available from
the GOODS, HDF, CNOC2, SDSS and other samples.

Next is the addition of the stars. Here we should take advantage of the
existing star maps, from the Hipparchos Tycho ( stars) or USNO-B1.0 (
stars) catalogs. The USNO-B1.0 catalog reaches to V=21 in two
bandpasses. Fainter than this, we will adopt a galaxy structure model
such as Bahcall-Soniera to predict the statistical distribution of
stars. The output of this is the star catalog.

Shear induced by intervening mass concentrations must be applied.
Collaborator Erin Sheldon is exploring shear measurement sensitivity and
systematics as a function of PSF and galaxy property.

Lastly, a model for the Galactic dust must be used to apply extinction
and reddening. This can be done using the Schlegel, Finkbeiner, and
Davis dust maps. The galaxy and star catalogs will be combined, and the
galactic dust model applied.

As an aside, cluster catalogs can be constructed from the galaxy
catalog, the shear maps, and the dark matter catalogs. One should also
extend this to SZE cluster samples.

**Instrument Model:** The survey simulation will break these galaxy+star
catalogs up into telescope pointing and time series information.

Once assigned a pointing, the stars and galaxies may be assigned to a
given CCD. This brings us to the instrument model. The instrument model
will be taken to start at the atmosphere and end at data reduction. It
will consist of :

• The weather model: correlations between time of year and the
atmosphere model

• The atmosphere model: sky brightness, seeing, zeropoints, extinction,
differential extinction

• The optics model: distortion due to the optics design, optics induced
PSF variations, temperature dependent filter effects

• The sensor model: QE, gain, read noise, bad columns, cosmic rays,
cross talk.

The output of this is a modified star/galaxy catalog containing the
instrumental effects as well as the cosmological objects.

Notable among the existing elements of the model is Steve Kent's optics
program, which can predict the PSF at any position in the focal plane
for the DES optics. Much of the sensor model work consists of working
with the CCD testing team and the photometric calibration team to
extract the necessary information. These include such straightforward
items as read noise and bad column maps to more complicated issues such
as system throughput.

### Image Level Simulations

For many purposes the catalog level simulation will suffice. For the
full mock data challenge, we will need to take the next step to full
image simulations.Given the catalog of objects, one places them into
artificial images. If one takes as a given that the catalogs contain
-all- of the information necessary to produce the images, then this step
contains much interpolation on the natural grid of galaxies. The
alternative is to use CCD level information packaged in a different way,
as will probably be most efficient for the spatially varying PSF that
can be predicted from the optics model.

We have begun exploring four well known packages available for
simulating optical/NIR astronomical images. These are the Shapelets
package written by Richard Massey, the ARTDATA package in IRAF, the
Terapix Skymaker package written by Emmanuel Bertin, and the SDSS
simSurvey package. In all four of these packages, simulated images are
generated using object lists of stars and galaxies. These lists can
either be supplied by the user or generated directly from the software
package. The relevant features of these codes:

**Shapelets:** The Shapelets code (Massey et al 2004) aims at producing
the most realistic galaxy morphologies, by decomposing the objects
detected in the Hubble Deep Field into their shapelet parameterizations
and then resampling the shapelet coefficients to build up realistic
simulated galaxies. The Shapelets code is in the IDL framework.

**ARTDATA:** ARTDATA is part of the IRAF astronomical image processing
package, and has been robustly tested by the astronomical community. It
can generate stellar images using moffat or gaussian profiles; in
addition, user-supplied profiles or images can be used to generate
stellar images. Galaxy images are simulated using either de Vaucouleurs
or exponential disk profiles; as with stellar images, user-supplied
profiles or images can also be used to generate galaxy images. ARTDATA
does not generate diffraction spikes, saturated objects, or bleeding
along columns, although cosmic rays can be simulated.

**Skymaker:** The Terapix Skymaker package generates the most realistic
instrumental effects. Skymaker includes the effects of tracking errors,
atmospheric seeing, diffraction spikes, and low-order aberrations into
its PSFs. Skymaker can also simulate saturation and charge bleeding
along columns. Galaxies are modeled using de Vaucouleurs profile bulges
and exponential disks. Skymaker is written in standard C, and the source
code is available for modification. Skymaker unfortunately has little
documentation, but descriptions of it can be found in Erben et al.
(2001) and Blaizot et al. (2003).

**simSurvey:** The SDSS simSurvey package was written to generate
simulated data to test the SDSS imaging pipelines. Stars and other point
sources (like asteroids) are simulated by delta functions which are then
convolved with instrumental and atmospheric PSFs. Galaxies are simulated
using de Vaucouleurs bulges and exponential disks. Power law wings can
be added to the image profiles. It is written in the SDSS
ASTROTOOLS/DERVISH software environment, which is based upon ANSI C and
Tcl/Tk. As a Fermilab in-house product, it would be easy to modify,
though major modifications may be required to use simSurvey for the Dark
Energy Survey simulations.

We will likely select the best components of each package. For example,
Steve Kent's program cray will generate PSFs from the DECam optical
model, be fed through Skymaker code to modify the PSF for a variety of
instrumental and atmospheric effects, and sent on to the Shapelets code
to generate the galaxies and stars. Our aim is not to produce another
simulation package, but rather to generate useful simulation data sets.

### Image Simulation Production

We are discussing a very large simulation, requiring much computation
and storage to create. We will take advantage of the GRID3 architecture
that Fermilab and the US DOE/NSF grid community is creating. This
features a shared security architecture that allows virtual
organizations, say the Dark Energy Survey Simulation Team, to access all
of the computing and storage of a distributed grid of compute clusters.

We will use a workflow management system, Chimera, that allows us to
both manage the production side of running the jobs on the compute farms
and to track the provenance of the data we create. We will use the
Storage Resource Management (SRM) tools to shepherd the data during the
simulation construction and during the transfer of the data to NCSA for
data reduction. The aim here is to use the standards of the Virtual Data
Toolkit, including Globus, as a means of leveraging the large amounts of
work the core experiments of the Grid Physics Network (GriPhyN), the
International Virtual Data Grid Laboratory (iVDGL), and the Particle
Physics Data Grid (PPDG) (CMS, LIGO, SDSS, ATLAS, and others) have
contributed.This is a data intensive science application and we propose
joining the Grid3/Open Science Grid effort.

## The Mock Data Reduction Challenge (WBS 1.3.4)

A mock data challenge is a tool for testing the accuracy and robustness
of a data management system. This project component (WBS 1.3.4) is led
by Chris Stoughton and will produce the mock data needed for these
challenges, coordinate with the data management team (WBS 2.6) to carry
out the reduction of these data, and then analyze the results. The
results provide feedback that the data management team will use to
improve the system.

Three milestones drive the development of the mock data and the pace of
the data processing system development. All of the data produced at the
milestones will be processed through the data production system as it
exists at the milestone.

The three milestones:

-   One tile: multiple exposures in four filters to completely cover one
    tile at a time. This exercises data formats that define the
    detectors in the focal plane, readout system, telescope, image data
    and log files. At this first milestone, values for these
    configurations, as well as the read noise and sky level, are in
    place. The goal is to provide baseline versions of all of these
    files and sample data to baseline code development.

-   One night: several complete tilings, and some incomplete tilings.
    This tests the functionality of the databases to associate objects
    within single tiles and in areas overlapped by more than one tile.
    For completed tiles, run processing to produce the complete catalog.
    Exercise algorithms to plan future observing, especially in light of
    bad or incomplete data.  Measure performance of all systems to scale
    to full system.

-   One month: a full stress test of the data systems demonstrates that
    we can process data at the rate they are collected, and provide
    realistic benchmarks for data access methods for catalogs and files.

## The Mock Data Analysis Challenge (WBS 1.3.5)

Once catalogs are produced, the science must be extracted. Surveys allow
a broad range of science to be done, but here we focus on the 4 key
projects of the Dark Energy Survey. We can test the analysis pipelines
using the simulations and the results of the mock data reduction
challenge. We will test the analysis pipelines on a years worth of
simulated data before the survey begins. This is a commissioning task,
to be done in 2007-2008.

The 4 key projects are:(1) The evolution of the population of clusters
of galaxies out to z \> 1 (2) Weak lensing power spectra and
cross-correlation cosmography (3) Galaxy angular power spectra (4)
Supernovae out to z=0.8. All four projects depend on the existence of
good photometric redshifts. At the heart of photometric redshifts there
are two generic techniques. The first is polynomial fitting of observed
colors to redshifts. The second uses templates to predict the observed
colors. Both are being explored. The cluster counting projects and weak
lensing projects both require optimal shape measurement codes and shear
analysis packages. The cluster counting projects, the weak lensing
projects, and the galaxy angular power spectra projects all require
angular power spectra measurement techniques, often based on fast
correlation function calculations. There are analysis packages specific
to the individual key projects. For example, in optical cluster finding
there are two generic techniques to find optical clusters, using the
E/S0 ridgeline of red galaxies to locate clusters or to use a matched
spatial/luminosity filter on photometric redshift shells. There is the
also the problem of computing the selection function, which occurs both
using the simulations and by putting fake galaxies into the data stream
and reanalyzing the data.

The whole collaboration is tasked with developing the analysis codes
under the lead of the data management group. What we outline here is the
organizing of all of the packages to accept the catalogs from the data
reduction and produce the cosmological constraints we are interested in.

## Summary

We have described a reference design for the Dark Energy Survey Strategy
that meets our survey science goals and requirements. The reference
design represents our current thinking and choices for the survey
strategy. Further analysis may show that better options are available.
The design of the Dark Energy Survey Instrument will be discussed in
Chapter 5.

**References**

1.  Wester, W. (2004), DECam note, "Useful Observation Time at CTIO for
    the Dark Energy Camera Survey"

2.  Randall, D. (2003) "Climate Modeling with Spherical Geodesic Grids",
    Computing in Science and Engineering

3.  Wright, E.L. (1996), astro-ph/9612006

4.  Tegmark, M. (1997), ApJ, 480, L87

5.  Massey et al (2004), MNRAS 348, 214

6.  Erben et al (2001), A&A 366, 717

7.  Blaizot et al (2003), astro-ph/0309305

**5. The Dark Energy Survey Instrument**

# 5.1 The Reference Design {#the-reference-design .unnumbered}

This section describes the essential elements of the Reference Design
for the Dark Energy Survey Instrument. It includes the camera, cooling,
wide-field optical system, the data acquisition system (DAQ), and the
mechanical and electrical interfaces to the V. M. Blanco Telescope and
its infrastructure. The Reference Design is not intended to be the final
design, since we are still evaluating a number of technical choices.
Nevertheless, it has allowed us to develop a reasonably complete
enumeration of the tasks that must be executed in order to build the
Dark Energy Survey Instrument. In Section 5.2, we describe the present
configuration of the Blanco and in sections 5.3-5.17, we describe the
choices we made in the Reference Design and the issues that we must
resolve before proceeding to a final design. With the Reference Design
in mind, we have developed a Reference Work Breakdown Structure (WBS)
and identified the critical items that will require development before
production can start. We have made a preliminary estimate of the
schedule and the resources that will be needed for the construction of
the instrument and its interfaces to the Blanco as well as the
commissioning of the instrument in Cerro Tololo. This is provided in
Chapter 6. These initial estimates suggest that we should be able to
deliver a fully tested instrument with a fully integrated and tested DAQ
to CTIO in Jan. 2008.

**5.2 The Blanco Telescope**

The Blanco is an equatorial mount telescope with a flip cage at the
prime focus. A photograph of the Mayall telescope at Kitt Peak, which is
a near twin of the Blanco, is shown in Figure 5.1 and a drawing of the
layout of the optical components in the Blanco cage is shown in Figure
5.2. Together, they provide a perspective of the layout of components at
the Blanco prime focus.

In the present configuration on the Blanco, the light from the primary
mirror is focused onto Mosaic II, the existing wide-field imaging
camera, when the cage is in the position shown in Figures 5.1 and 5.2.
Mosaic II consists of eight 2k × 4k CCDs. An optical system corrects the
field of view over an angular diameter of 0.85 degrees. It consists of 4
lenses and an atmospheric dispersion corrector. The optical system,
filters, camera and the f/8 secondary mirror are all mounted on the cage
that is attached by the spider to a ring. When the cage is flipped
through an angle of 180 degrees into the other position, the f/8
secondary mirror reflects the primary beam on to the Cassegrain focus.
The prime focus plate scale at the Blanco is 55 μm/arcsec (1 meter/5
degrees). Table 5.1 provides a further enumeration of some of the
essential parameters of the Blanco telescope.

![](media/image8.png){width="3.0729166666666665in" height="3.25in"}

**Figure 5.1** Side view of the cage on the Mayall telescope. The Mosaic
camera and corrector are mounted inside the cage. The spider attaches
the barrel to a ring that is in turn attached to the main truss (white)
of the telescope. The ring can flip the orientation of the cage such
that the other end points toward the primary.

![](media/image9.png){width="5.977083333333334in"
height="2.9229166666666666in"}

**Figure 5.2** The cage and optical components of the Blanco 4m
telescope. The corrector lenses are contained in a barrel, which is
supported by a pedestal bolted to the centerpiece of the cage. The
secondary mirror is located on the far left.

**Table 5.1 Parameters of the Blanco Telescope and Facilities at the
Site**

+----------------------------------+-----------------------------------+
| Blanco location                  | Lat. 30^o^ 10' S,                 |
|                                  |                                   |
|                                  | Long. 70 ^o^ 49' W                |
|                                  |                                   |
|                                  | Altitude 2200 m (7200 ft)         |
+----------------------------------+-----------------------------------+
| Primary Mirror                   | 4m dia., 50cm thick, 15 ton       |
+----------------------------------+-----------------------------------+
| Telescope Mount                  | Equatorial                        |
+----------------------------------+-----------------------------------+
| Prime Focus                      | f/2.87                            |
+----------------------------------+-----------------------------------+
| Plate scale                      | 55 μm/arcsec                      |
+----------------------------------+-----------------------------------+
| Typical V-Band seeing FWHM       | 0.8 to 1.1 arcsec                 |
+----------------------------------+-----------------------------------+
| Outside Temp. Range              | -10 to +25 deg. C                 |
+----------------------------------+-----------------------------------+
| Slew time                        | 35 sec. for 1 or 2 deg. slews     |
+----------------------------------+-----------------------------------+
| Guider Update Rate               | 2 sec.                            |
+----------------------------------+-----------------------------------+
| Adj. Range needed for focus      | \~2 cm                            |
+----------------------------------+-----------------------------------+
| N2 plant on mountain top         | 9 lts/hr capacity,                |
|                                  |                                   |
|                                  | 50,000 lts annual consumption     |
|                                  |                                   |
|                                  | Cost \~ \$1/lt                    |
+----------------------------------+-----------------------------------+
| Power                            | 120V, 60hz in dome and cage       |
+----------------------------------+-----------------------------------+

We propose to replace the cage and everything inside it except for the
atmospheric dispersion corrector which is not needed for our survey due
to the proposed short exposure times and because the Dark Energy Survey
does not propose to observe at airmass greater than 1.5. We also propose
to replace the mechanical, electrical, and communication interfaces
between the cage and telescope. This project incorporates handling of
the f/8 secondary mirror in order to avoid compromising other
functionality of the telescope. To provide room for the camera vessel
and services, we may need to remove the f/8 secondary mirror from the
cage. In this case we will develop a repeatable precision mounting
system that will allow the mirror to be reinstalled during the day to
the required alignment tolerances and with minimal risk to the f/8
secondary.

The Mosaic II has two guide cameras in addition to the eight image CCDs.
The guide cameras provide small corrections for the telescope tracking
system so that a star image will stay in precisely the same location
during an exposure. In our Reference Design we have CCDs on the focal
plane devoted to guiding, along with additional CCDs devoted to a new
semi-automatic focusing system.

**5.3 Technical Specifications**

Our science program was presented in Chapter 2 and Chapter 3 showed how
those science goals translated into technical specifications. Chapter 4
outlined how the survey would be performed with an instrument that met
the technical requirements of Chapter 3. A brief summary of the science
goals and technical specifications is given below:

Science Requirement: Cover 5000 sq-degrees in the South Galactic Cap in
5 years (≤ 550 nights)

Technical requirement: 3 sq-degree camera (2.2 deg FOV)

Science Requirement: Measure galaxy cluster photometric redshifts to
z=1.0 and 0.5\* with δ*z* ≤ 0.02

Technical requirements:

a.  SDSS g, r, i, z filters covering 400nm to 1100nm

b.  Limiting magnitudes g=22.8, r= 23.4, i=24.0 z=23.6

c.  QE \> 50% in the z band

d.  Photometric calibration to 2%, enhanced goal to 1%

e.  Read noise \<10 e-

Science Requirement: A small and stable PSF

a.  Seeing \< 1.1" FWHM with median ≤ 0.9\'\' FWHM

b.  PSF stable to 0.1% over a 1 sq-cm (9 sq-arcmin) area

Technical requirement:

a.  Pixel size and optical plate scale which give \<0.3"/pixel

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```
c.  Optical, mechanical and telescope tracking which contribute \< 0.4"
    FWHM across focal plane in the i and z bands

d.  Focus chip area \> 17 sq-arcminutes

e.  Guide chip area \> 25-50 sq-arcminutes, 1 Hz readout rate

    **5.4 Overview of Dark Energy Survey Instrument Project**

**5.4.1 Introduction**

As mentioned earlier, we plan to replace the prime focus cage and
everything inside it. Figure 5.3 shows a solid model of the Reference
Design of the new cage, corrector and camera. The camera vacuum vessel
contains the CCD focal plane, the liquid nitrogen cooling system, and
all power and signal feedthroughs. Cables will be routed to two crates
containing the front end electronics. In our current design, all the
cryogenic and cable connections are made through the sides of the
cryostat. This facilitates the camera assembly process and does not
interfere with the existing f/8 secondary mirror mounting system. If it
is necessary to provide more space for the camera and associated
services, we propose to remove the f/8 secondary mirror from the back of
the cage and make a repeatable mount system for it on the front of the
cage. Further design of the electronics, the camera vessel, and the
layout in the cage will show if this is necessary.

The optical configuration consists of five active lens elements and four
interchangeable filters. The corrector components are shown in Fig. 5.3
along with a filter storage and changing system. The filters are stored
outside the optical path, but within the corrector support barrel. The
camera vessel is aligned to and supported by the corrector barrel. A
shutter is located directly in front of the camera window.

![](media/image10.png){width="6.001388888888889in"
height="4.175694444444445in"}

**Figure 5.3** Proposed layout of the prime focus cage with the 2.1
degree corrector (lenses are white), filters (red is in the optical
path, blue and yellow are shown in their storage locations), shutter
(blue), camera vessel (grey) and 2 crates (purple) for data acquisition
electronics (may not be needed).

**5.4.2 Organization (Level 2)**

The Dark Energy Survey Instrument Project has been divided into seven
distinct Level 2 subprojects. Figure 5.4 shows the organizational
structure of the project at Level 2.

Instrument Management, WBS 1.1, is where all the management costs
associated with the instrument project are accumulated, including the
Management Committee costs relevant to the instrument. This also covers
costs associated with the preparation of schedule and costs, management
and specification of technical requirements, active tracking of progress
of the project (periodic reports) and reviews. WBS 1.2, Instrument
Construction, covers the design and construction of the camera, cage,
corrector and all associated equipment. Most of the remainder of this
chapter is devoted to describing this element and what we call our
Reference Design for the instrument. Survey Strategy, WBS 1.3, covers
the strategy for collection of the images and was described in detail in
Chapter 4.

![](media/image11.png){width="6.331944444444445in"
height="3.2006944444444443in"}

**Figure 5.4** Work Breakdown Structure of the Dark Energy Survey
Instrument Project

Telescope Improvements, WBS 1.4, and Commissioning at CTIO, WBS 1.5,
represent activities at the Blanco site and are presented near the end
of this Chapter. System Integration, WBS 1.6, covers development and
implementation of interface control documentation for Level 2 systems
1.2 - 1.5 and the Level 3 subsystems within them. This also covers
review of the testing plans to make sure they are consistent with
specifications. Dark Energy Science, WBS 1.7, covers the support needed
for doing science and writing papers with the data.

**5.4.3 Organization for WBS 1.2, Dark Energy Survey Instrument
Construction**

The Instrument construction project, WBS 1.2, is divided into eleven
Level 3 sub-projects. These are:

1.  CCD procurement

2.  CCD packaging

3.  Front end electronics

4.  CCD testing and grading

5.  Data acquisition

6.  Focal plane, camera vessel

7.  Cooling

8.  Optics (corrector and filters)

9.  Prime focus cage and integration with all cryogenic, electrical, and
    mechanical systems

10. Auxiliary components

11. Assembly and testing

The corresponding organization chart is shown in Figure 5.5. The next
sections describe these subprojects and the choices we have made for the
Reference Design.

![](media/image12.png){width="3.765972222222222in" height="4.875in"}

**Figure 5.5**. Organization of the Dark Energy Instrument Construction
Project

**5.5 CCDs Properties and CCD Procurement Plans (WBS 1.2.1)**

**5.5.1 CCD Technical Specifications**

Our survey goals require that we have high quantum efficiency (QE) at
the near infrared wavelength of \~1000 nm. The standard astronomical
CCDs typically have a QE at this wavelength of 5-10% because the charge
collection region is 10-20 μm thick and the total device thickness is
often less than 50 μm. The absorption length in silicon is 205 μm at a
wavelength of 1000 nm and thus thick sensors are required for a better
QE at that wavelength. Photons at near infrared wavelengths will simply
pass through 50 μm of silicon. LBNL has already developed thick, fully
depleted, back illuminated CCDs 200-300 μm thick^1^. For our Reference
Design we use 250 μm thick LBNL CCDs. The QE of the LBNL CCDs is shown
in Figure 5.6 and compared to a standard thinned astronomical CCD and a
deep-depleted CCD^2^.

![](media/image13.wmf){width="6.395833333333333in"
height="3.428472222222222in"}

**Figure 5.6.** QE curves from different types of CCDs.

LBNL has produced a variety of CCDs for different projects and several
devices have already been deployed for astronomical use. Figure 5.7
shows a 6" diameter wafer with LBNL CCDs. For our Reference Design we
choose the 4-side buttable 2048 × 4096 CCD. These are the two largest
devices shown on the wafer. The development of this CCD is complete.
This CCD has 15 μm pixels, 2 readout channels and meets all of our
specifications as shown in Table 5.2. These devices have been clocked at
250 kpixel/s with a readout noise of 7 e- and 4 e- at a rate of 100
kpixel/s. At this rate the readout will take 17.5 sec which is well
within the 35sec slew time of the Blanco.

![](media/image14.png){width="4.002083333333333in"
height="2.817361111111111in"}

**Figure 5.7**. LBNL CCDs on a 6" wafer

Although we have not ruled out using commercially available CCDs, the
approach in our reference design is to assume we will use the LBNL fully
depleted, thick CCDs. We believe this maximizes the science possible
with the instrument while minimizing the R&D needed for this project by
using CCD designs that have already been proven. As discussed in Chapter
3, deep depleted CCDs are commercially available, but their QE in the
near-infrared region is about half that of the thick fully depleted LBNL
CCDs. They could be used for our survey, but the completeness of our
galaxy redshift catalogue would suffer.

Figure 5.8 shows our Reference Design for the focal plane layout with
the 2k × 4k 4-side buttable LBNL CCDs. It contains 62 CCDs and a total
tile active area of 3.0 deg^2^. This satisfies the requirements
described in Chapter 3 for the camera size.

**Table 5.2. CCD Specifications**

+------------------------+---------------------+----------------------+
|                        | LBNL CCD            | DECam requirements/  |
|                        | performance         |                      |
|                        |                     | Reference Design     |
+------------------------+---------------------+----------------------+
| Pixel array            | 2048 × 4096 pixels  | 2048 × 4096 pixels   |
+------------------------+---------------------+----------------------+
| Pixel size             | 15 μm × 15 μm       | 15 μm × 15 μm        |
|                        |                     | (nominal)            |
+------------------------+---------------------+----------------------+
| \<QE (400-700 nm)\>    | \~70%               | \>60%                |
+------------------------+---------------------+----------------------+
| \<QE (700-900 nm)\>    | \~90%               | \>80%                |
+------------------------+---------------------+----------------------+
| \<QE (900-1000 nm)\>   | \~60%               | \>50% at 1000 nm     |
+------------------------+---------------------+----------------------+
| Full well capacity     | 170,000 e^-^        | \>130,000 e^-^       |
+------------------------+---------------------+----------------------+
| Dark current           | 2 e^-^/hr/pixel at  | \<\~25 e^-^/hr/pixel |
|                        | --150^o^C           |                      |
+------------------------+---------------------+----------------------+
| Persistence            | Erase mechanism     | Erase mechanism      |
+------------------------+---------------------+----------------------+
| Read noise             | 7 e^-^ @ 250        | \< 10 e^-^           |
|                        | kpixel/s            |                      |
+------------------------+---------------------+----------------------+
| Charge Transfer        | \< 10^-6^           | \<10^-5^             |
| Inefficiency           |                     |                      |
+------------------------+---------------------+----------------------+
| Charge diffusion       | 8 μm                | \< 10 μm             |
+------------------------+---------------------+----------------------+
| Linearity              | Better than 1%      | 1%                   |
+------------------------+---------------------+----------------------+

![](media/image15.jpeg){width="5.334722222222222in" height="4.025in"}

**Figure 5.8**. Focal plane layout using 62 2k × 4k LBNL CCDs and 8
additional small CCDs for guiding and focus. The dark (red) line shows
the outline of the tile area.

**5.5.2 Guide and Focus CCDs**

LBNL has developed a variety of small CCDs. For our Reference Design we
have selected a 982 × 935 pixel device with 15 μm pixels. These are
shown along the top and bottom of the focal plane layout in Figure 5.8.
At the nominal rate of 250 kpixel/s these can read out in 2 sec. For
guiding however, we expect to be able to tolerate higher noise and will
operate at a higher readout rate. Once the guide stars are identified,
it is also typical to read out only a small number of the surrounding
pixels, further reducing the readout time.

For focus our Reference Design has two sets of these chips installed \~
200 μm above and below the mosaic focal plane. As the telescope drifts
out of focus for the image CCDs, the focus in one or the other set of
chips will get better. The focus chips will be read out with the image
CCDs. The focus is thus monitored with every image and can be adjusted
as necessary.

These CCDs can easily be incorporated into the new mask needed for the
2k × 4k devices by using the leftover space on the 6" wafer.

**5.5.3 CCD Acquisition Plan**

LBNL has produced and packaged the 2k × 4k devices we have chosen in
small quantities^4^ and these devices are in use in telescopes at
UCO/Lick Mount Hamilton and Kitt Peak National Observatory with great
success. A production model for CCD fabrication has been established in
which the silicon wafers are manufactured by a commercial vendor and
delivered to LBNL for final processing. The steps performed by LBNL
include thinning to 250 μm from the 650 μm thick wafers supplied by the
vendor, application of the backside layers and antireflective coatings,
and finally etching and application of the Al for the front-side
electrodes and bond pads. Through discussions with our collaborators at
LBNL we have developed a conservative CCD procurement and delivery cost
and schedule. In our model, LBNL oversees the production process at the
foundry, receives the wafers, performs the final processing and tests
the wafers on a cold probe station. These tests are performed at -40 ºC
and test for shorts, opens, broken readout amplifiers and warm or hot
columns. LBNL is responsible for dicing the wafers into the individual
devices and shipping all the unpackaged devices to Fermilab along with
the testing results. Fermilab will perform the packaging and subsequent
testing.

We have avoided extensive development on the CCDs by basing our plans on
the existing 2k × 4k design. However, the CCDs have a lead time of
approximately 10 months for delivery of processed, unpackaged CCDs and
the yield is as yet unknown. At the time of writing, experience is
limited to a few devices; out of a total of 10 devices which were
packaged in a picture frame but not thinned or backside coated, 6 have
been determined to be good quality. For our purposes we will assume a
yield of 25% and develop a CCD acquisition plan based on this number. In
the plan outlined below we determine the yield before committing to the
majority of the CCD processing costs and thus significant cost and
schedule saving will be possible if the yield is higher. Contingency
plans have been developed to cover the additional cost and schedule
issues if the yield is lower than 25% and this is discussed in Chapter
6.

Discussion with LBNL and our experience with the construction of silicon
vertex detectors leads us to believe that it will require a dedicated
effort to develop CCD packaging skills and techniques at Fermilab and to
develop an efficient CCD packaging production line. We will begin this
process as soon as possible to reduce the possible risks later in the
project.

Our CCD acquisition strategy proceeds in 4 phases:

-   Phase A: Sept. 04, order mechanical parts for packaging studies, and
    4 unpackaged engineering grade electrically functional CCDs, \~3
    month delivery time

-   Phase B: Sept. 04 design new mask with four instead of two 2k × 4k
    CCDs/wafer, plus as many 1k × 1k devices that fit. Order
    preproduction parts and process \~2 wafers, est. 10 month delivery =
    July 2005

-   Phase C: Aug. 05, process the remaining wafers, \~3 month delivery,
    establish yield

-   Phase D: Aug. 05, place production order, \~8 month delivery

Phase A is the purchase of a large number of mechanical CCDs (blank
silicon with the top layer of metal for wirebonding studies), as well as
four unpackaged devices. These will be used to develop our packaging
techniques, tooling and procedures, assess adhesives, and study thermal
behavior and mechanical warping during cooling down.

The initiation of Phase B (preproduction) is on the critical path for
the project and is a key step towards reducing both cost and schedule
risk. In Phase B we ask LBNL to generate the preproduction masks for our
project and place an initial minimum order for 24 wafers. The existing
masks for the 2k × 4k CCDs have only two of these devices per wafer. The
new mask would use the same, proven, 2k × 4k CCD design and would have
four devices per wafer as well as 1k × 1k devices that fit in the empty
spaces. The delivery schedule is \~10 months after the masks have been
approved by the foundry. These 10 months consists of 7 months at the
foundry and 3 months for processing at LBNL. On receipt of the wafers at
LBNL (\~May 2005) only \~2 wafers will be processed for use in our
initial CCD acceptance and packaging studies. The remainder of the
wafers will be set aside for phase C. Delivery of phase B CCDs to
Fermilab is anticipated in August 2005. These will be packaged,
evaluated and tested with the goal of proceeding with Phase C and the
production order (Phase D) before the end of August 2005.

In Phase C, we will authorize LBNL to process the wafers that were set
aside in Phase B. Based on LBNL experience we assume that out of the
initial 24 wafers, 2 will be lost due to breakage and 2 more will be
used by the foundry for processing tests. Phase B processing used 2
wafers, leaving 18 wafers for processing in phase C. The wafers would
already be at LBNL and thus delivery of these devices would begin 3
months ARO. LBNL is estimating that they can process and test 5
wafers/month, thus delivering 20 tested devices/month to Fermilab. This
represents roughly one quarter of the processing capacity of the LBNL
Micron Systems Lab. These CCDs would start to arrive at FNAL in
\~November 2005 and finish in February 2006. They would be used to start
the ramp up to full production packaging and to determine the yield. The
testing information provided by LBNL will be used to determine the order
in which the CCDs are packaged. Initially in Phase C we will package the
lower quality parts and reserve the high quality parts for after the
packaging process is perfected. With these assumptions and 25% yield, we
will have 54 low quality parts for packaging development and 18 high
quality parts that could be used in the focal plane.

The final phase, D, is the production run of 3 lots of 24 wafers each.
Unless significant problems were found from the preproduction run and
analysis of the packaged Phase B parts, phase D would use the
preproduction wafer mask. For our initial planning purposes we assume
that 20 out of each 24 wafer/lot will be available to us for a total of
60 wafers. With the assumption of 25% yield we will need to process all
60 wafers. We thus anticipate delivery of 240 CCDs to Fermilab. The LBNL
delivery rate will be 20 tested devices per month for a total delivery
time of 12 months. As in Phase C, we will use the testing results
provided by LBNL to determine the order in which CCDs will be packaged.
The Phase D wafer order would be placed in August 2005 with the delivery
of fully processed tested parts to Fermilab expected 8 months later
(April 2006) continuing to April 2007.

Processing and packaging of the Phase C parts will indicate if our
assumptions of the yield are correct. If the actual yield is higher than
25% we can reduce the total cost by limiting the number of Phase D
wafers to be processed at LBNL. This would also accelerate the schedule.
If the actual yield is less than 25% we have sufficient cost contingency
to cover an additional 24 wafer lot order. This order would be placed
after analysis of Phase C was completed (\~March 2006).

One way to advance the schedule beyond what is presented above is to
increase the Phase B order to 2 lots (48 wafers) and to proceed with
continuous processing as soon as they arrive at LBNL. This could
potentially gain 5-6 months in the schedule. The cost and risks
associated with this approach will be discussed in Chapter 6.

**5.6 CCD Packaging (WBS 1.2.2)**

CCD sensors are packaged into modules that provide an electrical
interface and mounting features. The CCD package consists of a sensor, a
readout hybrid, and a mounting foot. Our Reference Design for the CCD
package is based on the package developed at LBNL^4^. The readout hybrid
is glued to the front (non-optical) surface of the sensor and has a pad
layout designed to allow wirebonding connections to pads on the CCD that
extend out beyond the edges of the hybrid, as shown in Figure 5.9. The
board is also equipped with an electrical connector. A few small surface
mount components, such as a temperature sensor, will be added very near
the connector location. Aluminum nitride ceramic is to be used for the
hybrid substrate since its thermal expansion characteristics are similar
to that of silicon.

![](media/image16.png){width="3.671527777777778in"
height="1.8444444444444446in"}

**Figure 5.9.** A readout hybrid board packaged with a 2k × 4k CCD

A mounting foot is also glued to this assembly, as shown in Figure 5.10,
in order to provide structural support and precision mounting engagement
to the focal plane support plate. It is constructed of Invar to provide
a stiff, thermally-stable framework for the module. A clearance hole
permits access to the connector on the hybrid. Mounting pins engage
locating holes in the focal plane support plate and set the module
position. One pin is designed with a diamond-shaped cross-section to
permit ease of installation for small variations in machining
tolerances. Pin lengths are longer than the overall thickness of the
foot / readout hybrid / sensor package in order to guide the module
accurately past its neighbors during installation. Threaded holes in the
foot permit attachment with screws from the back side of the focal plane
support plate. The pin and threaded hole volume are vented in order to
eliminate trapped volumes that can act as virtual leaks inside the
evacuated camera environment.

![](media/image17.png){width="3.671527777777778in"
height="2.5555555555555554in"}

**Figure 5.10.** A fully packaged 2k x 4k CCD module

CCDs are typically operated at temperatures of -90 to -120 ºC, however,
they must be glued to the support and readout hybrid at approximately
room temperature. This wide temperature difference requires careful
consideration of the thermal expansion properties of all the materials
involved, as flatness of the focal plane is important for the camera to
achieve a good focus. As described in Reference 4, LBNL has demonstrated
packaging of 2k × 4k CCDs.

For construction of our camera, we need to develop packaging techniques
and establish a production model at Fermilab that can handle a large
number of CCDs. The Silicon Detector Facility at Fermilab already has
significant expertise in bonding and gluing ceramic parts to silicon
sensors as a result of the construction of the numerous silicon vertex
detectors for the Fermilab Tevatron collider program. We believe that
CCD packaging will be a natural extension of that experience.

Each CCD module will be constructed in several steps. The first is to
assemble the components and connector onto the readout hybrid. Second,
the readout hybrid is epoxied to the front (non-optical) surface of the
CCD sensor. Fixturing that holds the parts with vacuum is used to
control the flatness and relative positioning of the two pieces based on
their physical edges. Spacers that are part of this tooling set the
thickness of the epoxy joint. After curing, the electrical connections
between the readout hybrid and the CCD are wirebonded. A test is then
performed to verify the basic functionality of the module. Fixturing is
then used to attach the foot assembly to the readout hybrid with an
epoxy joint. This tooling is used to precisely control the overall
package height in order to maintain uniformity of the focal plane
planarity. It also accurately locates the foot mounting pins relative to
the physical edges of the CCD sensor in order to ensure that the edges
of the assembled module do not violate the clearance gap between
adjacent modules. After epoxy curing, the CCD package is complete. Due
to the height of the foot assembly, wirebonding repair is not possible
on a completed package.

During production, a delivery rate of 20 CCDs per month translates into
an average throughput of one module per day. The assembly steps
discussed above are each anticipated to require only a few hours of
technician time, but they typically require a one-day curing time for
the epoxy to reach satisfactory handling strength. Therefore, it is
possible to construct modules with a one-per-day throughput with a
single set of fixtures. However, two sets will be needed to ensure that
this average capacity could be sustained over an extended period.

**5.6.1 Guide and Focus CCDs**

As discussed in Chapter 3, an area of \~40 sq. arcmin would typically
have \~three bright stars which are useable for guiding and/or focusing.
LBNL has developed a variety of small CCD options. For our Reference
Design we have selected a 982×935 device with 15 μm pixels. Each CCD
covers 18.6 sq. arcmin and has two readout channels. Four of these CCDs
would be used for each of the focusing and guide systems.

The packaging of these units is anticipated to be similar to the 2k × 4k
CCD modules, with its own aluminum-nitride readout hybrid board and
mounting foot design. These devices will not be 4-side buttable as the
connector covers a significant fraction of the CCD itself. Both thicker
and thinner mounting feet will be fabricated for the focus CCDs. With
adjustable assembly fixturing these modules can then be constructed with
the desired total heights.

**5.7 Front-end Electronics (WBS 1.2.3)**

The front-end (FE) electronics provides the interface between the CCD
sensors and the Monsoon data acquisition system (DAQ) (WBS 1.2.5).
Figure 5.11 shows a block diagram of the reference design for the FE
electronics. The proposed scheme provides for electrically isolated
optical signal transmission between the FE electronics attached to the
camera cryostat and the DAQ system located off the telescope. Within the
cryostat, the FE electronics will include the design of the circuitry on
the aluminum nitride (AlN) hybrid used to back each CCD sensor. A flex
cable circuit will connect the hybrid to a printed circuit board that
also forms the vacuum seal (vacuum seal board). The flex cable will
carry active components that switch clock lines and that preamplify the
CCD analog outputs.

The vacuum seal board will receive flex cables from each device and
route signals from inside to outside of the vacuum. Cables will provide
connection to modules that reside in compact-PCI crates attached to the
cryostat. These modules will function to provide the correlated double
sampling of the analog signals, analog to digital conversion,
distribution of clock signals and control lines, and power distribution.
The temperature of the FE electronics will be controlled so that thermal
air currents from the electronics will not impact the seeing. The
compact-PCI crate will also provide the optical drivers to send signals
to the Monsoon DAQ that is located off of the telescope.

An alternative implementation would be to locate the full Monsoon DAQ
crates on the cryostat and interface directly with the vacuum seal
board. In this implementation, Monsoon detector head electronics (DHE)
modules process and digitize the analog signal. It is thought that this
alternative implementation requires enclosed crates and more stringent
thermal management than the current reference design. A decision between
the alternatives will be made after experience is obtained operating
LBNL CCDs and the Monsoon DAQ electronics.

![](media/image18.png){width="6.498611111111111in" height="3.03125in"}

**Figure 5.11**. Schematic drawing of the FE electronics reference
design.

1.  **Requirements for the Front-end Electronics**

The requirements for the FE electronics are briefly described. For the
Dark Energy Survey, the requirements are relatively loose compared with
other CCD systems. The FE electronics must accommodate the
characteristics of the LBNL CCDs. The output signal from the CCD has an
expected level between 0-1V corresponding to the expected well depth of
170K electrons. The requirement on the FE electronics system noise is to
be less than 5e^-^ so as not to dominate the noise from the CCD devices
at 250 kpixel/s. The electronics system must maintain linearity over the
dynamic range at a level of 0.25% so as not to dominate the total system
linearity limit of \~0.5%, which is required for precise photometry. The
electronics needs to be compatible with a readout rate of 250 kpixel/s
over the entire CCD array so that a focal plane image can be read out
faster than 20 sec, less than the time required for slewing the
telescope. These requirements have been achieved on other CCD systems. A
key challenge for the FE electronics is to meet or exceed these
requirements with such a large CCD focal plane array. The enhanced
design goal of the front-end electronics is to exceed these rather loose
requirements so that the instrument has the widest possible use.

**5.7.2 Aluminum Nitride Hybrid**

The aluminum nitride (AlN) hybrid functions to bring the required pads
of the CCD device to a connector. The hybrid is part of the four-side
buttable packaging effort (WBS 1.2.2) to manufacture CCD modules. We
describe the aspects of this board that are relevant to the front-end
electronics. Pads on the CCD sensor are connected to traces on the
hybrid through wirebonds. In addition to traces, the hybrid will have a
good reference ground plane that should benefit the LBNL CCDs. Other
large-scale CCD systems have had performance compromised through the
effect of "substrate bounce" that would be less likely to occur in the
presence of a good ground plane. As for the electrical traces, the AlN
circuit will route the various lines from the CCD device to the
connector on the AlN board. Two trace layers are required in order to
tie various clock lines together to minimize pin count. Each hybrid will
have space for a temperature sensor to be mounted but only a fraction of
the devices on the final focal plane will be required to have a
temperature read out.

**5.7.3 Flexible Cable**

The flexible cable interfaces between the connector on the back of each
CCD module's AlN board and the vacuum seal printed circuit board. The
design of the flexible cable will emphasize several features that are
directed towards the requirements of the FE electronics. The flex cable
will carry the reference ground in a separate conducting plane. Trace
widths will be determined on the basis of proper impedance for signal
propagation and for voltage levels including the bias. The cable will
allow for passive components to perform necessary bypassing and
filtering.

The reference design of the flex cable includes a section that will be
stiffened to allow for the mounting of active components. We are
especially concerned with driving the clock lines (with high switching
currents) over a long cable. Putting solid-state switches on this
stiffened section so that the clocks going to the device can be
generated and driven locally mitigates this concern. Another concern is
the low-noise transport of the analog output and the protection of the
output transistor. Putting preamplifiers on the flexible cable would
mitigate both of these concerns.

Any component mounted on the flexible cable will be considered for use
in the cryogenic environment. Of particular concern is use of components
at extreme temperatures. While a thermal model for the temperature on
the cable hasn't been performed, we have begun an investigation as to
the suitability of different components at extreme temperatures. In this
investigation, we have found information used by the space community
that lists known problem components as well as successes at low
temperatures. We plan to limit parts inside the cryostat to those with
well understood outgassing and hygroscopic properties, with the goal of
avoiding the deposition of water or other foreign material on the CCDs.

Finally, we recognize that any electronics inside the cryostat must be
robust against failure since it will be difficult to service the inside
of the cryostat once the camera is commissioned. The Reference Design
reflects the concerns that have been mentioned. Testing of individual
and multiple devices will be done to determine the need for active
components inside the cryostat. If these active components are not
required for the FE electronics to meet the requirements, then the
flexible cable will not carry them and other functions such as
protection will move to the vacuum seal board.

4.  **Vacuum Seal Board**

The vacuum seal board is a printed circuit board and provides connectors
that accept the flexible cables inside the cryostat. As shown in Figure
5.12, the board is mounted into a metal frame by sliding though a slot
in the frame on one edge. It is potted (glued) to the frame, and then
vacuum-side connectors are assembled onto the board. The board's frame
is then installed on a flange on the cryostat and covered with an o-ring
sealed cap. The board thus has regions both inside and outside the
cryostat and conducting traces on the board function as the feedthroughs
of the cryostat wall, as shown in Figure 5.12. These traces will be
appropriately multiplexed and will terminate into connectors that will
be used to bring signals to electronics that sit in compact-PCI crates.
The vacuum seal board will be designed based upon experience at Fermilab
in the design of a similar board for the BTeV project.

![](media/image19.png) ![](media/image20.png)

**Figure 5.12** Electronics feed-through using the Vacuum Seal Board

**5.7.5 Camera Vessel Crate Electronics**

Cables from the vacuum seal board will carry signals to printed circuit
boards that will reside in compact-PCI crates mounted on the cryostat.
These crate electronics include boards that perform a variety of
functions for the FE electronics. The analog signal lines from each CCD
will undergo correlated double sampling (CDS) in order to reduce noise
from the reset amplifier. In this sampling scheme, a charge sensitive
amplifier will sense the analog line both during an integration window
during which a signal may be present and during an equivalent time
window when no signal is present. An electrical subtraction removes
correlated noise that is generated when the reset switch of the output
transistor is turned on and off. The subtracted signal will then be
converted to a 16-bit digital signal using a commercial low noise
analog-to-digital converter (ADC) chip. The digitized data will be
serialized and sent over a high-speed serial optical link to the
interface to the Monsoon DAQ system. The control of the CDS and
digitization and data formatting will be accomplished by suitably sized
field programmable gate arrays (FPGAs). The data stream will include
digitized levels from temperature sensors and other similar monitors.

Clock signals and control lines will also be distributed by the crate
electronics. The generation of these signals (the sequencing) will be
handled by the DAQ. The generated clock signals will be converted by the
crate electronics into the logic levels that are sent into the cryostat
to control the solid-state switches that generate the actual clocks that
go to the CCDs.

The crate electronics will include circuitry that distributes all the
required voltages for the CCDs. These voltages will be derived from a
voltage source that is the output of an isolation transformer. The
transformer will isolate a voltage source that comes up the telescope in
order to separate the electronics in the cage from other electrical
circuits that may be susceptible to undesired transients or subject to
fluctuations due to nearby lightning strikes.

**5.7.6 Interface to the Monsoon Data Acquisition**

The interface to the Monsoon data acquisition consists of circuitry that
makes all the signals compatible with the Monsoon system. In particular,
this board will send and receive optical transmission to the FE
electronics. This interface board is likely to be connected to the
backplane of the Monsoon crates. This board is at the border between FE
electronics and DAQ. Depending on the complexity of this board in its
final design and whether the final implementation is chosen to have
Monsoon crates on or off the telescope will determine whether this
interface printed circuit board remains under FE electronics or whether
it becomes a DAQ task.

**5.8 CCD Testing and Grading (WBS 1.2.4)**

Testing and grading CCD activities include an initial phase where CCDs
are exercised and characterized and a production phase where these
activities are used to classify the production devices to determine the
best ones that should populate the focal plane. The initial phase is
important to develop the infrastructure and experience so that the
production phase may begin when production devices first become
available. The production phase will be designed to test up to 20
devices per month, with some capacity to absorb bursts of higher
delivery rates.

In the initial phase, a CCD test stand will be developed that
incorporates the Monsoon data acquisition system. Various optical
devices and test and measurement equipment for CCD characterization will
be added and incorporated into the testing lab. As the test stand
develops, it will also be used for evaluating FE electronic design
options. After the testing program is developed, a production testing
procedure will be documented and devices will be tested and graded with
proper bookkeeping, travelers, and a database of test results. These
results will form the basis of an initial calibration of each sensor.

The testing and grading task is required to make a comparison of the
test results against the device requirements to insure that each CCD
device that is installed meets the minimum requirements for use in the
DES. The task is also required to assign a grade so that devices with
the highest grades above the minimum requirements will be used.

1.  **Test Stand Overview**

Figure 5.13 shows a block diagram of the test stand that will be
commissioned. The various optical and DAQ components will be used to
perform a sequence of measurements on the CCD device under test. The
test stand provides a variety of measurement conditions. Dark images can
be obtained by reading out the CCD when the shutter is closed. Uniform
illuminated images can be obtained by reading out the CCD when the
shutter is open and broadband light from the integrating sphere is shown
on the device. A calibrated photo-diode will provide an absolute
intensity measurement of the light. Exposure of the CCD to Fe^55^ also
can be used for charge transfer efficiency measurements. The
monochrometer and filters can be used to select a narrow band of
wavelength for illumination onto the device for quantum efficiency
measurements.

For production testing, two additional test stands will be commissioned
to increase the testing throughput. Ideally, all three test stands will
be configured equally in order to benefit from test stand software
development. After visual inspection, the production testing is
estimated to take two full days per device. The device is first
carefully loaded into the test cryostat and cooled down. The first day
of testing will record image data under various conditions. On the
second day, scans to measure the quantum efficiency will be made. Since
the QE depends on temperature especially at the longer wavelengths, the
QE vs temperature will be measured. Each device will be thermally cycled
and checked to better insure robustness against thermal cycling.
Finally, the device will be warmed up, removed from the test dewar, and
testing data will be checked.

![](media/image21.wmf){width="6.0in"
height="4.463888888888889in"}**Figure 5.13** Diagram of the test stand.

**5.8.2 Test Dewar**

The test cryostat or dewar, seen in Fig. 5.14, is designed to cool down
and warm up rapidly for fast turn-around of the CCD tests. This dewar is
patterned off of a similar dewar in use at LBNL in the CCD evaluation
lab. The dewar consists of a copper thermal shield in a vacuum box that
is in contact with a boiler column cooled by liquid nitrogen. The level
of liquid nitrogen in the boiler is self-regulating due to gas pressure
built up in the vertical column. A fused silica window allows light on
the CCD when a door is open.

Fast cooldown is initiated by opening an exhaust valve, allowing liquid
nitrogen to directly cool a cold mass and the CCD bolted to it. After a
few minutes, when the CCD is cold, the valve is shut, and the cold mass
and CCD come to equilibrium at a temperature established by the balance
between a thermally conducting foil connecting the cold mass to the
boiler and the various heat sources. This foil is sized to establish the
equilibrium temperature slightly below the desired operating point. A
small electrical heater embedded in the cold mass maintains the test
temperature.

Illumination enters the end of a light-tight cylinder that surrounds the
window. There is also an Fe-55 X-ray source for independent calibration
of the CCD. After the tests are complete, warm up is accomplished by
evacuating the liquid nitrogen from the boiler and heating the shield
and CCD with electrical heaters until they are safely above the dew
point.

![](media/image22.jpeg){width="2.134027777777778in"
height="2.727777777777778in"}
![](media/image23.jpeg){width="1.9381944444444446in"
height="2.727777777777778in"}

**Figure 5.14** 3D model of the test cryostat. A light tight cylinder
surrounds a fused silica vacuum window (left). On the right, the front
cover is removed to show the cold plate on which CCD devices can be
mounted.

3.  **Testing Procedure**

The exact testing procedure and acceptance criteria will be specified
after experience is obtained using the test stand to operate and
characterize LBNL CCDs. Listed below are a variety of tests that can be
performed using the test stand described above. For the Dark Energy
Survey, the strategy of recording multiple images over the survey area
relaxes the need for perfect devices. A study has been performed that
introduces approximately 100 consecutive bad columns randomly for each
of 62 devices -- thought to represent a worse case. The net result after
five tilings is that only about 1.5% of the survey area is imaged less
than three times, which is acceptable. The enhanced goal of the testing
is to select devices that exceed the minimal CCD requirements. As a
guide, we will strive to select devices that fall into the top grades
defined by the number of bad columns assuming the devices are otherwise
fully functional and that other defects affect fewer pixels than a
column (4096). The grading scheme motivated by another large focal plane
(Megacam) specified acceptable devices as:

Science Grade 1: Fewer than 6 bad columns

Science Grade 2: Fewer than 12 bad columns

Science Grade 3: Fewer than 20 bad columns

Engineering Grade: functional, non-science grade device

Mechanical Grade: non-functional

*Visual Inspection*

Each bare CCD needs to be visually inspected for gross flaws and
particle contamination before it is mounted and tested. This will be
done with a low power inspection microscope. Devices that show gross
defects will be classified as non-science grade.

*Pixel, Column, Row Defects*

Pixel defects are defined as any CCD pixel that has a response outside
of a predefined range for a given input light intensity. Pixel defects
can take the form of a dead pixel (no or little response) or a hot pixel
(large response for little input). Defective pixels can also be defined
as a pixel with a response that is far from the average pixel response
of a good CCD pixel. Column and row defects are defined as any column or
row that has a response outside of a predefined range. Pixel defects can
be found by taking many CCD flat field images at a constant low light
level then finding the mean and sigma of each pixel over all the images.
Bad pixels have a large sigma or a mean outside the average.

*Linearity*

CCD pixel response over a range of illumination intensities allows one
to measure the linearity of the response of the CCD. Multiple images are
taken at a number of different illumination levels over the full dynamic
range of the CCD, using constant integration times (or a constant
illumination level and a range of exposure times). Illumination
intensity is measured with the integrating sphere photodiode and is
plotted versus average pixel response over a stable CCD region. A linear
fit is applied and the maximum deviation from the fit defines the CCD
linearity.

*Pixel Full Well, Serial Register, Parallel Registers Full Well
Capacities*

The full well capacity can be measured by either spatially varying the
illumination intensity across the CCD or by illuminating the CCD during
readout. Since this is just a check of available dynamic range, the
measurement need not be very precise. With all of these measurements,
care must be taken not to saturate the pixel, serial register and
amplifier before saturating the acquisition ADC. This can be done by
limiting the gain from CCD to ADC.

*Pixel Full Well Capacity*

Flat field illuminate the entire CCD at the beginning of readout such
that the CCD will saturate by the end of the readout cycle. Pixels
across columns will have a response proportional to their order in
readout. A profile of the number of electrons per pixel versus column
will show a plateau which is the pixel full well capacity.

*Serial Register Capacity*

Similar to pixel full well capacity measurements except that one bins
rows together. This allows the serial register response to increase with
readout time up to saturation.

*Output Amplifier Capacity*

Similar to pixel full well capacity measurements except that one bins
rows together along with pixels along row to form an N x M region of
pixels input to the output amplifiers.

*Charge Transfer Efficiency*

Charge transfer efficiency (CTE) is a measure of how efficiently a CCD
moves charge from pixel to serial register to output amplifier.
High-quality imaging requires that large CCDs have a very high CTE. An
accurate method for measuring CTE is to use a Fe^55^ source that
produces a precise number of electrons in a pixel.

The CCD under test is exposed to an Fe^55^ source with an activity level
that produces a few 10's of X-rays per second per cm^2^. Many images are
acquired of these X-ray exposures. Since each X-ray produces either 1616
electrons (5.9 keV X-ray) or 1778 electrons (6.2 keV X-ray), a known
quantity of electrons is produced per pixel. Pixel CTE can be measured
by comparing the response of pixels near the serial register to pixels
far from the serial register. Serial register CTE can be measured in a
similar way by comparing signals near the output amplifier to those far
from the output amplifier.

In addition, Fe^55^ measurements give a value of the conversion factor
of pixel electrons to ADC counts. This conversion factor can be found
from the mean of the Fe^55^ signal histogram of pixels near the output
amplifier.

*Quantum Efficiency*

This is normally a difficult and time consuming measurement requiring
measurement of absolute pixel response over several narrow optical
bandpasses at, possibly, several temperatures. The measurement proceeds
by passing the broadband optical source of the test stand through a
narrow band monochromator to select a measurement frequency. The CCD is
then illuminated by this source and the CCD and integrating sphere
calibrated photodiode responses are acquired. In general, the calibrated
photodiode response gives a measure of photons per second per cm^2^ at
the CCD surface. The quantum efficiency, as a function of wavelength,
can then be recorded as the ratio of the number of electrons measured to
the number of incident photons.

However, there is a quartz optical window between the integrating sphere
and the CCD which must be accounted for. This is done by placing a
second calibrated photodiode inside the cryostat in place of the CCD and
measuring its response compared to the integrating sphere photodiode
response for each measurement wavelength. This gives a response
measurement of the quartz window which can be used to determine the
photons per second per cm^2^ on the CCD surface.

*Dark Current*

Dark current is a measure of the random thermal signal produced in each
pixel. At operating temperatures and exposure times selected for the
Dark Energy Camera, these signals are very small and therefore difficult
to measure. Also, cosmic rays must be removed and stray light
contamination must be eliminated. Small dark current signals can be
measured by binning several pixels into one pseudo-pixel before being
read out. Dark current can then be measured by taking the pseudo-pixel
signal difference between a short (1 second) and long dark exposure (few
hours) and dividing by the number of binned pixels and the number of
seconds in the long exposure minus the short exposure.

*Read Noise*

Read noise is a combination of noise of the external electronics and
noise of the CCD output amplifier. Read noise can be determined from
several zero integration time, dark field images. The zero integration
time eliminates dark current effects while the dark field eliminates
light induced signals.

*Thermal Cycling*

During the testing process, each CCD module will be thermal cycled
between ambient and operating temperatures several times to identify any
weaknesses in the fabrication process.

4.  **Database Software and Travelers**

The test results will be recorded into a web-based database and
information of each device tested will be recorded on a traveler
document that stays with each device. Similar tracking tools for
production testing have been developed at Fermilab for other projects.
We plan on adapting these tools for the use of CCD testing.

Each CCD will be assigned a grade that indicates its potential end use.
Devices that are functional but have too many defects or other problems
will be classified as engineering grade. Devices that meet the
scientific requirements will be classified as science grade devices.
Testing data will be further analyzed so that best science grade devices
are the ones that are used on the focal plane.

**5.9 Data Acquisition (WBS 1.2.5)**

The data acquisition (DAQ) serves two functions. One is to convey data
from the sensors to permanent storage for subsequent (off-line)
analysis. The other is to monitor and control the telescope and
environmental parameters that determine image quality. The DAQ contains
both hardware and software components. It must be able to respond to
commands issued by the personnel operating the telescope. It must also
be able to operate autonomously, maintaining optimal telescope behavior
during routine operations and responding appropriately to abnormal
conditions.

The data acquisition will be a collaborative endeavor led by the
University of Illinois and involving Fermilab, LBNL, and NOAO. NOAO has
developed a data acquisition framework, called Monsoon, which we propose
to adapt to this project. This approach carries two advantages over an
entirely new design. It reduces the cost and time to completion of the
project by taking advantage of previous work. It also maintains and
extends a standard for astronomical data acquisition, reducing the costs
of future projects.

We will use the data acquisition during instrument fabrication and
assembly as well as during survey operations. The former use, in "test
stands," not only will permit testing of the data acquisition, but also
will provide a test facility for the CCDs and focal plane. Test stand
operation requires that the DAQ be operational well before the
instrument as a whole. We will begin by using an unmodified Monsoon
system, replacing components with their production versions as they
become available.

In the existing Monsoon system, CCD digitization is performed in
detector head electronics (DHE) crates by acquisition (ACQ) cards, which
must reside within about a meter of the CCDs in order to minimize noise.
Each DHE crate also contains a clock/control board (CCB) that sends
clock signals and bias voltages to the CCDs and a master control board
(MCB) that communicates with the outside world and controls the ACQs and
CCBs.

In the production DAQ that we plan to build, the analog functionality
will be separated from the digital, allowing us to move much of the
electronics off the telescope. Figure 5.15 shows block diagrams of the
two architectures. Image sensors include both the main image CCDs and
the focus CCDs, which are read out together. Guide CCDs are read out
separately. The left diagram shows the existing Monsoon architecture.
The right diagram shows our modified architecture. The DHE is a crate
with a cPCI backplane. The PANs (pixel acquisition nodes) and supervisor
are processes running on PCs.

![](media/image24.wmf){width="6.496527777777778in"
height="2.8201388888888888in"}

**Figure 5.15** Block diagrams of data and control signal flow.

**5.9.1 Data Acquisition Test Stands (WBS 1.2.5.1)**

The first use of data acquisition will be in test stands that will be
used to verify the functionality and measure the performance of CCDs as
they are produced by LBNL and packaged by Fermilab. The test stands will
also be used in DAQ software and hardware development.

To match the CCD development schedule, these test stands must be
functioning by October 2004. In order to meet this schedule, they will
consist of unmodified Monsoon DAQ systems. We already have the NOAO
software in hand, and two sets of hardware (sufficient to read 8 CCDs
each) have been ordered by UIUC for delivery in September 2004. The time
before hardware delivery is being used to evaluate the capabilities of
the system, to ensure that all required diagnostic capabilities exist.
In particular, to simplify the data analysis, the test conditions must
be included as metadata in the FITS files that Monsoon produces. FITS
(Flexible Image Transport System) is an astronomy standard, and much
existing analysis software can read data that is in this format.

As the new electronics becomes ready, we will verify its performance by
using it in the test stands. This means that testing of the focal plane
during assembly (starting in mid-2006) will use the production DAQ. Test
stand use drives the DAQ schedule. See the Management chapter for
schedule details. As well as being the initial test stand
implementation, the unmodified architecture also serves as a fallback
design, in case the new front-end electronics is not ready on schedule.
While less optimal (more expensive, and more space and power on the
telescope), the fallback would allow the DES instrument to operate.

**Table 5.3** Data Acquisition parameters. The front end to DAQ data
rate is the highest (burst) rate in the system. The data rate to storage
is significantly lower (*e.g.,* 9.7 MB/s for 100 sec image exposures).

  ------------------------------- ---------------------------------------
  **Image CCD Array** (+focus     
  CCDs)                           

  Number of CCDs                  62 (+4)

  Pixels per CCD                  2048×4096 (1024×1024)

  Amplifiers per CCD              2

  Pixel digitization rate         250 kHz

  Digitization time               17 sec

  Bytes per image                 971 MB

  Data rate (FE→DAQ)              57 MB/s

  **Guide CCD array**             

  Number of CCDs                  4

  Pixels per CCD                  1024×1024

  Amplifiers per CCD              2

  Pixel digitization rate         1 MHz

  Digitization time               0.5 sec

  Bytes per guide                 8 MB

  Data rate (FE→DAQ)              16 MB/s
  ------------------------------- ---------------------------------------

**5.9.2 Telescope Data Acquisition (WBS 1.2.5.2)**

The important DAQ parameters are listed in Table 5.3. The focal plane
contains 62 image CCDs and four focus CCDs (placed slightly out of the
focal plane). These 66 CCDs are read out together. The focal plane also
contains four guide CCDs that are read out during the exposure in order
to maintain telescope pointing accuracy.

The highest data rate, 57 MBps between the front end ADCs and the DHE
ACQ buffers, occurs during the 17 s required to digitize the 971 MB of
CCD information. This is high by astronomy standards, but is not beyond
what particle physics experiments routinely achieve. The data rate is
determined by the 250 kHz CCD digitization rate. Because the data is
carried on 140 optical fibers, data flow is not a significant technical
issue. The DAQ could easily handle a 2 MHz digitization rate. The data
rate to permanent storage is much lower (9.7 MBps for 100 s image
exposures).

In the proposed architecture, CCD digitization is performed by custom
front end electronics to be developed at Fermilab. Digital data will
flow off the telescope on 140 optical fibers (one per ADC) to modified
ACQ cards in the DHE crates. The modified DHEs have no analog
functionality and do not directly control the readout. Readout control
is performed on the telescope in response to digital signals from the
DHE. This allows us to move all of the DAQ, except for ADCs, DACs, and
electrical/optical interfaces, off the telescope. Separating analog and
digital functionality retains the ability to minimize noise by keeping
the ADCs near the focal plane, while reducing the heat and space budget
by moving most electronics off the telescope. Because the current
Monsoon implementation performs both the analog and digital operations
in detector head electronics (DHE) crates, some electronics must be
redesigned, as described below.

The DAQ will accept the digitized CCD pixel information and deliver it
to permanent storage for further processing. The guide CCDs will be read
out once per second during image exposure, and real time processing will
include using guide stars to control the telescope pointing. Thus, there
will be two readout sequences, one for the image CCDs and one for the
guide CCDs. For organizational purposes, electronics on the telescope is
considered to be part of the front-end subsystem. The DAQ includes
hardware and software that resides off-telescope.

**5.9.2.1 DAQ Work Packages**

Although Monsoon is a working data acquisition framework and
implementation, we must make significant changes to accommodate our
hardware configuration:

-   Signal digitization will be performed outside the DHE crates, by the
    front end electronics.

-   The CCDs will be read out at two different rates, one for telescope
    guiding, and one for image acquisition.

Monsoon work must be closely coordinated with the design of the front
end electronics (WBS 1.2.3). This coordination will be one of the
principal responsibilities of the Data Acquisition Working Group (DAWG).

**5.9.2.2 DAQ Hardware** (see the Management chapter for a cost
itemization)

Much of the hardware will be a replication of the existing Monsoon
configuration. This includes two DHE crates, each with a Master Control
Board, a Clock/Bias Board, and several Acquisition boards. The system
also requires a Pixel Acquisition Node (PAN) PC and a Supervisor PC. We
have not yet determined how much of this functionality can reside one
computer and one electronics crate.

Some hardware must be modified or newly designed:

-   The use of the front end ADCs obviates digitization in the Monsoon
    detector head electronics (DHE). As a consequence, the DHE
    Acquisition cards will be redesigned and greatly simplified. They
    will become digital buffers.

-   The Clock/Bias Boards generate the digital control signals and
    analog voltages for the CCD focal plane array. We must redesign them
    to accommodate the LBNL CCDs. The functionality will be split in
    two: a card in the DHE crate will generate digital control signals
    that electronics on the telescope (part of the front-end) will turn
    into the analog signals needed for the CCD readout.

-   The on-cage interface converts the electrical data signals to
    optical for transmission off the telescope. It also receives
    (isolated) AC power from off-telescope and generates the appropriate
    DC voltages.

**5.9.2.3 DAQ Software**

The existing Monsoon software is functional, but we must adapt it to the
needs of our project. For example, the DAQ will actively control
telescope pointing (on a one second time scale) during exposure. The
real-time software must measure the point-spread functions of stars
imaged by the guide CCDs and send commands to the telescope controls.
Also, CCD diagnostics must be developed for the test stand application.
These diagnostics will then be modified for use as focal plane monitors
on the telescope. We will also develop graphical interfaces, both for
the description of the readout sequences and for the operational
displays.

**5.9.2.4 Fallback scenario**

As was mentioned above, in case the front end digitization development
does not proceed quickly enough to be useable on our timescale, the
fallback DAQ scenario is to use the existing Monsoon system with minor
modifications. This fallback will be more expensive, will dissipate more
power in the telescope cage (251W *vs.* about 60W), and will require
more space in the cage (two cPCI crates). These parameters are not
ideal, but would not fatally compromise DES performance. In this
scenario, the main hardware design work would be of a 16 (or higher)
channel ACQ board, to minimize space requirements. NOAO engineers have
already begun such a redesign, and we will assist them if it becomes
necessary.

#  {#section-1 .unnumbered}

# 5.10 Camera Cryostat Vessel and Focal Plane (WBS 1.2.6) {#camera-cryostat-vessel-and-focal-plane-wbs-1.2.6 .unnumbered}

This section describes the vacuum vessel that houses the CCD focal plane
and all the associated cooling and mechanical components. 

**5.10.1 Camera Vessel**

The Camera Vessel (cryostat), shown in Figure 5.16, consists of a front
plate, a forward spool piece that houses the supporting electronics, a
rear spool piece that contains the cooling package. The front plate of
the cryostat, which mounts to the corrector optics housing assembly,
contains the housing and vacuum seal for the C5 optical lens and
supports the focal plane plate with its CCD modules. The front plate of
the cryostat is attached to the forward spool piece, shown in Figure
5.17, which supports the cables, the cable vacuum feed-through board and
outer crates for the Front End Electronics (FE). The two FE crates are
mounted on opposite sides of the camera vessel. The two electronic
feed-through board enclosures are located between the crates. A vacuum
port and a port for a vacuum gauge are also mounted to the forward spool
piece.

The rear spool piece supports a liquid nitrogen dewar, as shown in
Figure 5.18. The dewar is thermally coupled to the focal plane via
copper braids. The dewar is cylindrical and the outer diameter is small
enough to leave access between the dewar and the cryostat wall for
attachment of the copper braids. Mounted to the back surface of the
dewar is a cell of Zeolite to help maintain good vacuum even with some
out-gassing from the cables and PC boards inside the cryostat. Ports are
attached to the spool piece for utilities such as liquid nitrogen dewar
supply and return lines, a vacuum port and a port to mount a vacuum
gage. The back of the rear spool piece is used for access during final
assembly and maintenance. The back plate used to close this opening has
no connection ports. The heat load to the dewar is reduced by polishing
or gold/nickel plating all of the internal metal surfaces and additional
internal radiation heat shields are not required. With two separate
spool piece modules, the camera portion of the cryostat can be
preassembled at the same time the cooling package is assembled and
integration of the two systems is simplified. The cryostat dimensions
are shown in Table 5.4.

![](media/image25.png)

**Figure 5.**16 Camera vessel assembly with vacuum seal boards for
signal feed-through and crates for electronics

![](media/image26.png)

**Figure 5.17** Camera vessel forward spool piece with vacuum seal
boards.

![](media/image27.png)

**Figure 5.18** Camera vessel rear spool piece with liquid nitrogen
dewar.

**Table 5.4 Cryostat Dimensions**

  ---------------------------------------------------- --------------- --------
  **Feature**                                          **Dimension**   

                                                       **Inch**        **mm**

  cryostat overall length                              30.0            762

  cryostat flange outer diameter (OD)                  32.0            813

  crates, between outside walls                        49.5            1257

  crates, OD of a wrapping cylinder                    52.4            1331

  boards, with cover, OD of a wrapping cylinder        41.2            1047

  Inner diameter of prime focus cage                   62.0            1575

  CCD mounting plate, between long flats               19.5            495

  CCD mounting plate, between short flats              20.6            523

  dewar total height                                   8.0             203

  dewar OD                                             16.0            406

  minimum distance between dewar and CCD support plate 5.0             127
  ---------------------------------------------------- --------------- --------

**5.10.2 Focal Plane**

The CCDs will be precisely located and attached to a large metal plate
called the focal\
plate.  This plate defines the interface between the CCDs and the camera
vessel.  The positions of the CCDs are set by engagement of locating
pins in the CCD module support feet with precision holes machined into
the focal plane support plate.  Additional holes are provided to permit
a screwed connection from the rear of the plate to fix the CCDs into
place and to provide access for the readout cable connection.  The focal
plane\
support plate will be several centimeters thick in order to provide a
stable support and limit the effect of gravitational sag to a few μm.

In order to maximize the active area density within an image, it is
desirable to minimize the gaps between adjacent CCD modules. The
closeness which can be achieved depends on the accuracy to which the
mounted module profile is known. A study of anticipated tolerances on
the modules, focal plane plate, and assembly jig concepts has been
performed assuming the following tolerances:

  -------------------------------------------------------- --------------
  Sensor size                                              \~15 μm

  Machined feature locations                               \~13 μm

  Machined hole diameters                                  \~13 μm

  Minimum pin / hole radial clearance:                     \~2 μm
  -------------------------------------------------------- --------------

The sequential stack-up of the various factors involved in the CCD
module assembly and mounting into the focal array found that the
location of an edge could vary by 124 μm when the individual factors
were added linearly and 42 μm when added in quadrature. For our
Reference Design we assume a gap of 250 μm is needed between modules to
accommodate these tolerances and allow for removal and or installation
of a CCD after the focal plane is completed.

To achieve the low noise necessary for astronomical applications, the
CCDs are typically cooled to -90 to -120 ºC.  For our Reference Design
we assume they need to be cooled to -100 ºC, although we will
investigate running them at warmer temperatures. The focal plate will be
thermally coupled through copper braids to a cold mass that is directly
cooled by a liquid nitrogen volume inside the vacuum vessel. Small
heating elements will be used to actively regulate the temperature of
the focal plate.  The focal plane support will be designed to keep
temperatures uniform to within a few degrees and to minimize distortions
resulting from temperature changes during cool down and from temperature
variations within the plate. This can be achieved by designing the
plate's mounting system to accommodate its contraction as it cools and
by designing the plate to have small thermal variations and stable
mechanical characteristics.

During assembly onto the focal plate, handling rods inserted into the
backs of the CCD modules aid in handling and installation by providing a
convenient handhold away from the sensitive electronics and by providing
guidance into the focal plate before the module alignment pins are
engaged. Once the module is in place, these installation rods are
replaced one-by-one with fasteners to fix the module in place. The
flexible module cables can be installed onto each module from the rear.
This cable will be clamped to the back surface of the focal plate in
order to provide strain relief and a thermal intercept for any heat
conducted along the length of the cable. The populated focal plate is
then assembled into the front half of the cryostat vessel, which
contains the C5 optics window and the vacuum feed-through boards. The
cables are connected to the feedthroughs and dressed in place, and then
the cooling module of the cryostat vessel is installed and connected to
the focal plate via copper braids. Finally, the back cover of the
cryostat is installed.

Once integrated into the completed camera vessel assembly, replacement
of individual CCD modules can be achieved with the camera cryostat
assembly removed from the telescope and placed in a clean work
environment. The rear spool vessel module of the cryostat and the C5
optical window package are then removed. With some CCD cables
disconnected for access, a CCD can be re-equipped with installation rods
and then extracted through the C5 window opening. The module can then be
replaced and recabled and the camera vessel reassembled.

**5.11 Cooling & Vacuum Systems (WBS 1.2.7)**

This section describes the systems that provide cooling to the CCD focal
plane and vacuum inside the camera cryostat vessel.

**5.11.1 Cooling System**

Figure 5.19 shows a sketch of the cooling system components located
within the camera vacuum vessel. These components include the focal
plate (CCD's mounted on the cold plate), a liquid nitrogen reservoir,
copper braids used to thermally connect the liquid nitrogen reservoir to
the cold plate, and zeolite to assist in maintaining a low vacuum level
in the vessel.

**5.11.1.1 Camera Heat Load**

The camera heat load is due primarily to the radiative heat load through
the window (\~50 Watts), the CCD's (\~20 Watts) and associated
electronics. The mechanical support of the focal plate will be from the
window end of the vacuum vessel and will be minimized by fabricating the
supports using a low conductivity material. The load due to the focal
plate supports is estimated at 10 Watts. The wiring and cables
contribute about 25 Watts. The radiative heat load other than that
realized through the window is another 20 Watts. The total of these
estimated heat loads is 125 Watts. Since detailed information about the
heat load from the electronics is not known and in the interest of
including a safety factor, a total heat load of 200 Watts in the camera
vessel is presumed at this time.

![](media/image28.png){width="5.666666666666667in"
height="3.7604166666666665in"}

**Figure 5.19** Schematic of the Dark Energy Camera Using Liquid
Nitrogen Cooling.

**5.11.1.2 Liquid Nitrogen Cooling System**

It is assumed in this discussion that the camera will be cooled with
liquid nitrogen. In order to avoid manually filling the liquid nitrogen
reservoir, an automatic filling system is planned. This system includes
a refrigeration system to cool and condense the nitrogen, a dewar to
store the liquid nitrogen, a liquid nitrogen pump, and transfer lines.
The supply and return transfer lines between the camera and the liquid
nitrogen storage dewar require a length of about 150 feet each. The
total heat load of the transfer lines is estimated at 140 Watts. The
liquid head of the supply line will range from 70 to 100 feet depending
on the camera position.

The heat load of the storage dewar must also be considered and is
estimated at 60 Watts. The total system heat load is thus estimated at
400 Watts. One appropriate refrigeration system for this heat load is
the Stirling refrigeration cycle. A system is available which produces
10 to 14 liters of liquid nitrogen per hour depending on the supply
pressure chosen.

Based on these estimates, a liquid nitrogen supply rate averaging seven
liters per hour will be required. The camera will use roughly five
liters per hour while the supply line will require two liters per hour
to remain cold. Taking into account some vaporization of the liquid
through the supply transfer line, the pump should be able to deliver to
the camera vessel at a rate of 1.4 liters per minute. If fill periods of
30 seconds each are used, 10 fills per hour are required. This implies
one fill every six minutes. This will maintain the camera liquid at a
fairly constant level, varying by only 0.5 liter.

The camera liquid nitrogen reservoir size is currently designed at 22
liters. By maintaining the liquid level in the reservoir at 10 liters, a
two hour reserve of liquid nitrogen is available at any given time. It
is possible to design a larger reservoir in order to provide a longer
reserve if deemed necessary. A cooling reserve is intended to provide
time to switch to a backup dewar of liquid nitrogen if maintenance on
the refrigeration system is required. Using a portable 160 liter backup
dewar would provide close to one full day of cooling.

The camera will be positioned in any of several angles during fills. By
positioning the supply and return points very near the center point of
the liquid nitrogen reservoir coupled with maintaining the maximum
liquid level slightly lower than one-half full (10 liters), flow of
nitrogen to and from the reservoir will not be impeded and liquid will
not drain into the nitrogen return line. The liquid will be pumped from
the storage dewar to the camera liquid nitrogen reservoir via a
submersible pump located inside the storage dewar. The pressure at the
pump outlet will be on the order of three atmospheres absolute while the
storage dewar will be maintained only slightly above one atmosphere. The
camera liquid nitrogen reservoir will also be only slightly higher than
atmospheric pressure.

The components of the refrigeration system are such that they are
separable and may be located apart from one another. The nitrogen
condenser and storage dewar must be located together, however, the
refrigeration system compressor may be located some distance away from
the condenser/dewar. The compressor is where heat is rejected from the
refrigeration system. Liquid coolant, already available at CTIO, is
required at the compressor in order to accept this heat and carry it
some distance away from the telescope before rejecting it to the
atmosphere.

**5.11.1.3 Consideration of a Cryo-Cooler Cooling System**

As an alternative to liquid nitrogen cooling, the possibility of using
cryo-coolers is being considered. Those types being examined include the
Gifford-McMahon, Pulse Tube, and Joule-Thomson type refrigeration
systems. Use of cryo-coolers to cool the camera brings the advantage of
lower power consumption, however, one must address the issues of cooling
capacity, vibration, maintenance, and space.

The Gifford-McMahon type offers a high cooling capacity independent of
its orientation but produces significant vibration. Units with a cooling
capacity of 300 Watts at 80K are available. The possibility of dampening
the vibration will be studied. The Pulse Tube type offers a good cooling
capacity (60 Watts at 80K) but multiple units would be required to cool
the camera. The Pulse Tube type has a relatively low vibration level but
is sensitive to orientation requiring its cold end to be pointed down.
Its sensitivity to position makes it an unlikely choice as the camera
must operate cold at many angles. The Joule-Thomson type cooler has the
lowest capacity (about 24 Watts at 120K) of these three types and would
require multiple units to keep the camera cooled. Even with multiple
units, we would be required to operate the coldest mass in the camera at
around 120 K. Its vibration is the lowest of all and its capacity is
independent of orientation.

With any type of cryo-cooler, actual maintenance on a unit will require
some camera downtime. Spare units may be installed for use in the case
that an operating unit fails but it would contribute to the camera heat
load when not in use. Swapping out units likely means another camera
vacuum pump-out period which could keep the camera out of service for
days.

Use of cryo-coolers requires that space is available inside and outside
of the camera vessel to install the quantity of units needed. Also, a
*cold mass* is required to provide a cooling reserve in place of the
liquid nitrogen reservoir. To provide an adequate cooling reserve, the
*cold mass* weight may necessitate a support system other than the
cryo-coolers. A support system will add to the camera heat load. These
issues will be addressed in our consideration of cryo-cooler use for
this camera.

**5.11.2 Vacuum System**

The vacuum system provides a vacuum better than 10^-6^ Torr in the
camera vessel. A roughing pump is used to achieve a vacuum of 10^-4^
Torr, then a turbo pump is used to achieve an initial vacuum better than
10^-6^ Torr. During camera operation, the cold mass inside the camera
vessel cryopumps water vapor and maintains the vessel vacuum without the
need of being connected to a mechanical pump. A vacuum valve on the
camera vessel allows isolation from the mechanical vacuum pump when the
telescope is operating. Opposite from the vacuum pump port on the camera
vessel is another port that supports a vacuum gage for continuous
monitoring of the vacuum.

**5.11.2.1. Outgassing**

The camera vessel contains PCB boards and cables that will outgas. The
outgassing rate is estimated in Table 5.5. The pumping speed required to
maintain 10^-7^ Torr is equal to 17,611 liters/sec. The water pumping
rate is 1.40×10^+5^ liters/sec/m^2^ at 77 K. The surface area required
is then 0.125 m^2^ LN2. The surface area of the liquid nitrogen dewar is
0.5 m^2^ which is four times larger than the required surface area to
maintain 10^-7^ Torr.

**Table 5.5. Gas Loading**

  ------------- --------------- -------------- -------------- -------------- ------------
                **Area**        **Quantity**   **Rate**       **Total Load** **Data**

  **Item**      **(cm**2**)**                  **Torr-l/s**   **Torr-l/s**   **Source**

  **Flex Cables 375             62             7.5×10^-8^     1.74×10^-3^    LHCb
  (Kapton)**                                                                 

  **Silicon     21              62             4×10^-9^       5.31×10^-6^    Pyrex value
  (CCDs)**                                                                   

  **Stiffened   6.3             62             1×10^-8^       3.91×10^-6^    BTeV note
  Flex Cable**                                                               2578 \*

  **Aluminum    1734            1              1×10^-10^      1.73×10^-7^    A. Roth
  Focal Plane**                                                              

  **Feed Thru   37              2              2×10^-9^       1.48×10^-7^    BTeV note
  boards**                                                                   2578 \*

  **C5 Optic    1963            1              4×10^-9^       7.85×10^-6^    Pyrex value
  Window**                                                                   

                                               **Total Load** 1.76×10^-3^    
                                                              Torr-l/sec     
  ------------- --------------- -------------- -------------- -------------- ------------

\* Outgassing rates are after 150 hours of pumping.

**5.11.2.2 Warming**

During either an intentional warm-up of the camera such as for a
scheduled maintenance or in the case of an unintended warm-up such as
the result of a power failure, the CCD surfaces must be maintained at a
temperature higher than the cold surfaces that act to cryo-pump the
vessel's vacuum environment. This minimizes the possibility of
condensing any material onto the CCD faces as the cold surfaces in the
camera warm up. The highest heat load to the camera is the radiative
load on the CCD array and focal plane, thus these surfaces remain warmer
than the hardware associated with the liquid nitrogen reservoir. The
heaters on the focal plate may be used to provide additional heat to the
focal plane and thus the CCD's. Use of these heaters during a power
failure requires an independent, backup power supply for the heaters and
temperature monitoring.

#  {#section-2 .Section-Heading}

**5.12 Optical System (WBS 1.2.8)**

**5.12.1 Introduction and Requirements**

An optical corrector is needed to achieve good image quality over the
2.2 degree diameter field of view of the camera.  Such a corrector will
require the use of large, custom-made glass lenses that have a long lead
time to acquire. Correctors of sizes approaching this size have been
built at the AAT, CFHT and Subaru telescopes. A number of design studies
also exist, including for LSST, with even larger optics than those
proposed here. We have adopted a baseline design that meets the
requirements for the Dark Energy Survey and otherwise minimizes the
technology and schedule risks associated with the procurement and
fabrication of the lenses.

The requirements for the corrector are listed in Chapter 3.  The basic
requirements are that it has a 2.2º diameter field of view, a wavelength
range of 0.39 to 1.1 μm, an image size (full width half maximum) of less
than 0.4", and a plate scale such that 15 μm pixels correspond to \<
0.3". Additionally, space must be available for a shutter and for
insertion of large glass filters.\
\
**5.12.2 Design\
**The adopted corrector design is shown in Figure 5.20.  The design
prescription is listed in Table 5.6.  It has five lenses, of which one
also serves as the window for the camera dewar, plus a glass filter. All
lenses are made of fused silica.  One surface has a mild aspheric shape;
the other surfaces are all either flat or spherical.  This design blends
together elements from two concept designs commissioned by CTIO, one
from PRIME Optics, the other from V.

Terebizh, but is actually more similar to two designs that have been
proposed for the Lowell 4 meter telescope (Epps and diVittoria 2003;
Blanco et al. 2003).

Figure 5.20. Corrector
Design![](media/image29.jpeg){width="5.191666666666666in"
height="4.65in"}

Several factors were involved in the process of developing the design.
These include:

a.  Few glass types are available at the sizes needed.  Fused silica
    blanks of the required size can be manufactured and delivered by
    Corning on a firm schedule.  The largest lens (C1) is thick enough
    that the blank will needed to be slumped first.

b.  Aspheric surfaces are more difficult and hence take longer and are
    more expensive to figure than spherical surfaces.

c.  The accuracy with which the curvature radii can be measured is
    limited by vendor\'s testing equipment; for example, Optical
    Sciences Center switches from a high accuracy to a lower accuracy
    method once the radii exceed 40 inches.

d.  Ghosting characteristics are often ignored by designers but can have
    an impact on data quality.  Small changes in design can amplify or
    mitigate the impact of ghosting.

Table 5.6. DES 2.2º Corrector Design

+-----------+------------------+------------------+-------------------+
| Surface   | Radius (mm)      | Thickness (mm)   | Glass             |
+-----------+------------------+------------------+-------------------+
| 1         | -21312           | -8556            | reflect Primary   |
|           |                  |                  |                   |
|           | ccon = -1.1      |                  |                   |
+-----------+------------------+------------------+-------------------+
| 2         | -888             | -90              | Fused Silica C1   |
+-----------+------------------+------------------+-------------------+
| 3         | -1003            | -778             | Air               |
+-----------+------------------+------------------+-------------------+
| 4         | -2360            | -50              | Fused Silica C2   |
+-----------+------------------+------------------+-------------------+
| 5         | -622             | -194             | Air               |
+-----------+------------------+------------------+-------------------+
| 6         | -10031           | -70              | Fused Silica C3   |
+-----------+------------------+------------------+-------------------+
| 7         | 3731             | -643             | Air               |
+-----------+------------------+------------------+-------------------+
| 8         | 0                | -15              | Fused Silica      |
|           |                  |                  | Filter            |
+-----------+------------------+------------------+-------------------+
| 9         | 0                | -10              | Air               |
+-----------+------------------+------------------+-------------------+
| 10        | -869             | -65              | Fused Silica C4   |
|           |                  |                  |                   |
|           | a4 = -3.15e-10   |                  |                   |
|           |                  |                  |                   |
|           | a6 = -9.48e-16\  |                  |                   |
|           | a8 = -4.83e-21   |                  |                   |
+-----------+------------------+------------------+-------------------+
| 11        | -3382            | -235             | Air               |
+-----------+------------------+------------------+-------------------+
| 12        | 1551             | -39              | Fused Silica C5   |
|           |                  |                  | (Window)          |
+-----------+------------------+------------------+-------------------+
| 13        | 1293             | -40              | Air               |
+-----------+------------------+------------------+-------------------+
| 14        | 0                | 0                | Focal Plane       |
+-----------+------------------+------------------+-------------------+

**5.12.3 Performance and Characteristics\
**Table 5.7 gives the image sizes averaged across the SDSS filter
bandpasses.  The worst-case value of D80 is .59", which exceeds the
requirement of .64".  In the r and i bands, where the best image quality
is sought for the weak lensing measurements, the equivalent FWHM is
D80/1.53 = .27" worst-case. This is the size of 1 pixel.

**Table 5.7 D80 (arcsec) for images in the 4 SDSS Filters**

**(as a function of focal plane radius)**

Radius          g r i z

0 0.32 0.11 0.17 0.31

45 0.35 0.19 0.21       0.34

90 0.39 0.14 0.22       0.33

135 0.40 0.21 0.25       0.33

180 0.50 0.32 0.36       0.42

226 0.59 0.37 0.41       0.47

The lens sizes are given in Table 5.8.  The clear aperture includes an
overfill of 1-2 mm.  The outer diameter includes a 20 mm edge to be used
for mounting and support.

**Table 5.8 Lens Sizes for the DES Corrector**

Lens Clear aperture (mm) Thickness (mm)   Thinness (mm) Outer Diameter
(mm)\
C1 1072.3 234.1 54.0     1112.3\
C2            736.7      153.1 50.0      776.7\
C3            683.4       70.0 48.6      723.4\
Filter          591.4       15.0 15.0      631.4\
C4            581.7       77.2 24.0      621.7\
C5 (Window) 486.2       57.9 35.2      526.2

**5.12.4 Finite Element Analyses**

A handful of analyses have been done to identify any possible critical
problems in the design due to the size and weight of the lenses.  So
far, none have been found.  The design is tolerant of a wide range of
thicknesses for each lens, so any problems encountered in the future
(but before procurement of the blanks) could likely be corrected without
a major redesign.\
\
The change in shape of each lens due to gravitational loading was
calculated and found to have negligible impact on the design.  The most
critical lens is the one forming the dewar window, since it must support
the differential between sea-level atmospheric pressure and vacuum. The
current thickness in the design is 38.7 mm. A finite element model was
constructed with the IDEAS program to study the deflection of the window
(Derylo May, 2004). The result is shown in Figure 5.21. The midpoint
deflection is 55 μm which is marginally important for the design. The
maximum stress is 4.4 MPa, which yields a factor-of-safety over 11 for
the flexura strength value of 50 MPa reported for fused quartz material.

l ![](media/image30.png){width="4.098611111111111in"
height="2.9055555555555554in"}

**Figure 5.21** Expected Deflection of C5 optic based on and FEA
analysis

**5.12.5 Tolerancing and Assembly**

The design is robust against small inaccuracies in the lens curvatures,
thicknesses, and refractive indices provided that these quantities are
measured accurately after fabrication and compensated by adjusting the
positions of the lenses during assembly.

A preliminary tolerance analysis has been done to identify possible
problem areas in fabrication or assembly.  The figure of merit used for
the analysis was a weighted average of r.m.s. spot size across the focal
plane as measured in the four SDSS filter bandpasses.  Factors that were
investigated included positioning errors (translation and tilt),
curvature errors including both fabrication and measurement, refractive
index errors, inhomogeneities in the glass, and primary mirror alignment
errors. A sensitivity analysis was run in which an allowed degradation
of performance was assigned to each variable, and the maximum
permissible error of that variable determined.  These values were
compared against numbers taken from standard practice or specification
sheets.  For one corrector element, C1, the radius of curvature of one
surface was reduced slightly so it could be tested more accurately. 
Otherwise it was found that standard practice would always do as well as
or better than required. The overall contribution to image degradation
is estimated to be .24" worst-case in D80 (which is added in quadrature
to all other contributions).

**5.12.6 Coatings**

Coatings for the optics are necessary in order to reduce reflection
losses at air-glass interfaces. In the Reference Design using fused
silica, each uncoated surface produces a loss of \~3.5%. Without
coatings, the total loss through the 5-element corrector would be about
30%. A nominal goal is coatings on all surfaces which reduce reflection
losses to 1% or better, per surface, yielding an acceptable total loss
of about 10%.

A second reason for minimizing reflection losses is the impact on
ghosting. The obvious effect is to reduce the intensity of all ghosts,
but it secondarily allows for greater flexibility in the optical design
since less of the total \"optimization budget\" must be spent on
minimizing ghosts optically if the coatings are good.

There are two basic options often used in astronomical applications for
coating optics this large. The preferred method for the first 4 elements
(C1-C4) of the corrector is some form of Sol-Gel coating. This is a
semi-hard coating with excellent performance which is relatively easy to
deposit on optics this large, and has the advantage that it can be
cleaned\
off and reapplied if it gets damaged. The other option is a hard
dielectric coating. The simplest would be a 1/4 wave of MgF~2~, with
better-performing multi-layer coatings being a possible option on all
but the largest element (C1). The dewar window (C5) must have some form
of hard dielectric coating to ensure no outgassing into the vacuumn
dewar. A final possible option for coating most of the optics is a
hybrid solgel coating. This recently developed coating technology
combines an undercoat of MgF~2~ with a tuned Sol-Gel overcoat and
provides spectacular performance (losses less than 0.5% over the optical
waveband). These coatings are currently significantly more expensive,
and can only be sourced from one supplier, but we may reasonably expect
prices and availability to improve in the coming few years. Also, CTIO
has developed a facility for Sol-Gel coating and has recently put
Sol-Gel on MgF~2~ for the elements of the SOAR Optical Imager, with
excellent results. Study on scaling up this facility to the larger DES
optics is under way. A final decision on coatings will require a
detailed analysis of the costs and benefits regarding both ghosting and
throughput.

**5.12.7 Ghosting\
**The high reflectivity of CCDs combined with the large number of
surfaces in the design mean that ghost images of single stars and the
diffuse night sky from the focal plane deserve attention.  It is assumed
that the LBL CCDs reflect 15% of the incident light, the Sol-Gel
coatings reflect 0.8%, and MgF~2~ coatings reflect 1.5%.

Ghost images of stars create an annular halo of light around but not
centered on each star image.  The best strategy for minimizing their
impact depends somewhat on one\'s science program.  For studying large,
extended objects, one might want to minimize the size of the halo so
that the sky background over large areas is kept uniform.  For studying
small\
sources, one might want to maximize the size of the halo (but reduce its
surface brightness) so one can measure accurate local sky backgrounds.
The latter strategy is chosen here.  To achieve this, the dewar window
is located 40 mm from the focal plane.  This diffuses the ghost image of
a point source to a diameter of 27 mm (480 arcsec).  A 1^st^ magnitude
star produces a ghost that has a surface brightness of 21 mag/sq-arcsec,
about as much as the night sky.

The ghost image of the exit pupil is formed by reflections of the night
sky between the CCDs and the ensemble of all surfaces in the corrector.
If one is careless in the design, it is possible to create an in-focus
image of the exit pupil that requires extra software in data processing
to remove (which happened with the Mayall 4 m prime focus corrector). A
composite ghost image was computed and is shown in Figure 5.22. The
dominant contribution comes from the leading surface of lens C4, which
acts to focus reflected light back onto the focal plane. The peak
intensity is about 6% of the incident light in the center of the field,
falling to less than 1% at the edges.

![](media/image31.jpeg){width="4.498611111111111in" height="3.23125in"}

**Figure 5.22** Computed ghost image in the Dark Energy Camera. The
square grid of smaller images is a computational artifact and should be
ignored.

**5.12.8 Filter Characteristics**

We adopted the SDSS g, r and i filters for our Reference Design since
they will provide the required accuracy in the determination of the
photometric redshifts of our target galaxies. As noted earlier, our z
band red cutoff differs slightly from the SDSS z band. While both are
effectively defined by the red cutoff of the silicon, the thick, high
resistivity silicon CCDs that we plan to use have a much higher QE in
the z band than the SDSS CCDs. The filter characteristics were selected
to bracket the Calcium H and K break at 394 nm in the spectrum of red
galaxies, which is typical of galaxies in clusters. At 0.05 redshift the
H and K break is at 410 nm, just blue-ward of blue cutoff (50%) of the g
band filter. At a redshift of 1.1 the break is at 830nm, just red-ward
of the red edge (50%) of the i filter. Note that we want filters to
bracket both sides of the H and K break in order to derive good
photometric redshifts; in particular the z filter is necessary at
redshifts near one in order to provide coverage red-ward of the H and K
break.  The proposed characteristics of the filters are shown in Table
5.9.

Table 5.9. The Proposed Wavelength Characteristics of the Filters

  ----------- -------------------- --------------------- -----------------
  Name        Center (nm)          FWHM (nm)             Trans. (%)

  g           480                  140                   0.91

  r           625                  140                   0.97

  i           770                  150                   0.98

  z           950                  240                   0.98
  ----------- -------------------- --------------------- -----------------

**5.13 Prime Focus Cage and Corrector Housing (WBS 1.2.9)**

**5.13.1 Prime Focus Cage**

Figure 5.23 shows a detailed view of the cage. The corners of the cage
represent the interface to the existing spider. This joint marks the
outer mechanical boundary of what we plan to replace. The internal
layout of the cage has been designed with ease of servicing in mind. The
corrector is supported from the center of the cage. A focusing mechanism
is part of the interface between the cage and the corrector. The linear
bearings internal to the cage allow the corrector to be separated from
the camera and moved towards the primary. This will allow access to the
shutter on the camera face and to the filter changing mechanism. Cables
and cooling tubes from the floor of the telescope will follow the
trusses and spider to the left end of the cage. Development of a clear
understanding of the cable and cooling tube routes, and how to deal with
the cage rotation, is a critical aspect of the integration of our
proposed design with the existing infrastructure at the Blanco. Our
Reference Design has the cooling tubes and the CCD cable connections on
the sides of the vessel.

A major advantage of replacing the entire cage and all of its service
functions is that it minimizes the complexity of the interfaces with the
Blanco telescope. In addition, it allows us to assemble the entire
camera and all of its services in a self-contained module that can be
completely tested before shipment and installation at Cerro Tololo. This
means that the integration effort is localized and is a far simpler task
than if the new camera were installed piece by piece into the existing
cage. We have a Level 2 sub project devoted to integration and are in
close contact with our NOAO representative (T. Abbott) for discussions
of integration issues. As the Reference Design progresses, an
integration and configuration control committee will be established to
provide the organizational structure necessary for successful
integration of the Dark Energy Survey Instrument with the infrastructure
at the Blanco.

**5.13.2 Corrector Barrel**

The corrector barrel is a series of cylinders, one per lens, four in
number; lens C5 is mounted in the camera dewar. These cylinders will be
made of either forged steel, aluminum or Carbon fiber. Forgings
eliminate many internal stresses that can be developed during welding
processes, thereby minimizing warping during machining. Finished
components fabricated from forgings tend to be stronger and more
dimensionally stable than welded components. Use of carbon fiber in the
long cylinders could reduce the effects of temperature changes on the
corrector focus. Fermilab has experience with and equipment for
fabrication of this sort of carbon fiber structure. The cost difference
between components fabricated of either forgings or weldments is minor.
In some cases, due to shape complexity, forgings offer a considerable
cost benefit. The material choice will be determined by the effect of
the thermal expansion on the relative positions of the lenses and on a
cost benefit analysis. The system is designed to allow assembly in
either a vertical or horizontal orientation within the cage assembly.
Once mounted to the telescope, assembly and disassembly can be
accomplished with the aid of fixtures mounted to one of the prime focus
cage end-rings.

![](media/image32.wmf){width="5.995138888888889in"
height="2.436111111111111in"}

**Figure 5.23** Corrector barrel in the prime focus cage

Interior and exterior finish is to be bead-blasted and painted flat
black for steel, and bead-blasted and anodized for aluminum.

Weight comparison for aluminum or steel construction:

Camera with both corrector and cage fabricated of steel: 11625 lbs. =
5275 kg

Camera with both corrector and cage fabricated of aluminum: 5643 lbs. =
2560 kg

The entire corrector will be continuously purged with dry nitrogen to
provide a stable and dry atmosphere for corrector elements and coatings.
In particular, the C5 lens will be cooled via radiative heat transfer
from the CCD focal array and will experience condensation if the purge
is not maintained. Heaters may also be installed around the edges of
this lens if further analysis shows that this is necessary to eliminate
condensation.

**5.13.3 Lens Mounting System**

Lenses will be "potted" around their circumferences into Invar-36 rings
(figure 5.24). These have CTE very close to that of fused silica and
will offer a mounting platform for the lens flexure ring. These flexure
rings will ensure that the lenses remain within centering tolerances
while compensating for differential expansion between barrel and lens.

The potting material will be some form of Silicone product and will be
specified as we approach the lens mounting in the production schedule.

![](media/image33.wmf){width="5.25in" height="3.1708333333333334in"}

**Figure 5.24** Lens mounting ring detail.

The Invar rings will incorporate a set of safety rims to ensure lens
capture, should the Silicone bond fail.

This design, as mentioned above, will maintain the lenses on the optical
center but will move with the barrel material parallel to the optical
axis. This mounting system can be dimensionally altered to accommodate
the final lens configuration after lens fabrication is completed.

**5.13.4 Filter Changing Mechanism**

Typically there will be multiple filter changes per night. For example,
when the moon rises, the i and z band observing are not affected, while
g and r are significantly degraded. This will motivate a filter change.
We are designing a filter changing system which will make it possible to
change the filters quickly and reliably. The time estimated for a filter
change is ≤ 5 sec. and it will be possible to perform a filter change
during slewing. The large space between element C4 and C3 is the optimum
location for the filters and the filter changing mechanism. The
implications of this choice are 1) these filters are quite large,
\~560mm (\~22") diameter and 2) the guide and focus CCDs, which are
located on the focal plane with the image CCDs, will see filtered light
and thus must also be sensitive in the z band.

A modular approach is used for the filter changing mechanism. Cutting a
set of four slots into the C4 barrel cylinder and mounting a guide rail
inside this cylinder will allow easy installation and removal of each
filter mechanism (Figure 5.25). These filter housings will be attached
with a plate, employing threaded fasteners, to the slot side the
corrector barrel. The opposite end of the filter housing will anchor to
a receiving plate mounted to the opposite side of the barrel from the
entry slot.

![](media/image34.wmf){width="5.997916666666667in"
height="2.502083333333333in"}**Figure 5.25** Filter changing mechanism

The filter housing will provide mounting surfaces for the filter locking
pins, stop switches and motion dampeners. The drive actuators will be
Moog gear head DC brushless motors with sufficient torque and speed to
move the filters through their arc within 5 seconds.

We have incorporated a series of safety interlocks into the filter
design. Encoders mounted on the motors will determine filter position
and will be monitored by a controller. This controller will allow only
one filter to move at a time. A torque limiting coupling, located
between the actuator output shaft and filter mounting ring pivot shaft
will prevent damage in case of collision with either another filter or
its own storage ring. The receiving rings for both in-use and stowed
filter positions will have limit switches wired in series to stop motor
rotation. Two acceleration dampeners per filter per direction will be
installed to prevent excessive contact forces between the filter
mounting rings and their receiving rings in both the in-use and stowed
positions. Locking mechanisms will be employed on the receiving rings to
ensure that the filter positions remain stable when in-use or in the
stowed position while the telescope is in motion. This will also allow
the actuator motors to be de-energized, minimizing heat-loading around
the cage and corrector environment. The modular design will allow the
filters to be removed and new filters installed in approximately an
hour.

**5.13.5 Focusing Mechanism**

The focusing ring will be mounted to the same struts that comprise the
tip-tilt adjustment system described below. This is done to ensure that
the focusing ring rotational axis remains parallel to the corrector's
optical axis when tip-tilt adjustments are made (Figure 5.26).

![](media/image35.wmf){width="5.075in" height="3.170138888888889in"}

**Figure 5.26** Focusing mechanism

An anti-backlash pinion for a worm gear segment mounted to the outside
circumference of the ring and actuated by a small Moog gearhead motor
will drive the ring. The ring will be supported through three sets of
two flanged track rollers preloaded against each other on the outer rim
of the ring and mounted to the adjustment struts through a
strut-connecting ring. Axis motion parallel to the optical axis will be
generated by a set of three ramps machined into the focus ring at 120º
spacing and consuming a 90º arc on the surface of the ring. The ramps
will rise 1/2" from the neutral surface of the focus ring. These ramps
will be the guide surfaces for three sets of two preloaded clowned tract
rollers acting against each other through ramp surfaces. These tract
rollers will be mounted to the outside circumference of lens cylinder C2
and also spaced 120 deg apart. This system will allow extremely accurate
and smooth movement of the focusing ring and will not require the gear
motor to remain energized to hold position regardless of telescope
motion.

**5.13.6 Lateral and/or Tip-tilt Adjustment Mechanism**

At this time, we do not know whether it will be necessary to adjust the
transverse and tip-tilt for the focal plane actively (while the
telescope is in motion). For our Reference Design we have a system that
can be set up to do manual adjustments or, by incorporating a set of
actuators, can be used to do active adjustments while slewing to a sky
position. The tip-tilt is adjusted through the use of cams mounted on
each end of the tip-tilt support struts (figure 5.27). There will be
three of these strut units mounted between the cage and the corrector
barrel, separated by 120º spacing. One end of the strut will be attached
to the corrector through a set of bars housing linear bearings sliding
on a shaft mounted to the outer circumference of lens cylinder C3. These
linear bearings and shaft are part of the focusing mechanism.

![](media/image36.wmf){width="5.997222222222222in"
height="3.642361111111111in"}

**Figure 5.27** Lateral adjustment system

The opposite end of the strut unit will consist of a T-shaped bar
mounted to cage rings. This T-bar will extend into an oversized slot,
machined into the linear bearing bar. Two shafts, with cams machined
onto them, will pass through the T-bar at opposite ends and be mounted
into a spherical bearing that will allow shaft movement off the
perpendicular axis to the T-bar surface. These same camshafts will
extend through journals in the two tangs of the oversized slot in the
bearing bar. A lever arm attached to one end of the camshafts can effect
lateral movement between the two strut bars.

If used manually, the level arms are adjusted and the struts locked into
position. If active adjustment is required, an actuator can be
incorporated to move the lever arms. Should we need only lateral
motion - movement perpendicular to the optical axis - a single actuator
operating a strut's two lever arms would accomplish the task. If both
tip-tilt and lateral motion become necessary, then using an actuator per
lever arm will meet the need. This system can also be used to preload
the linear bearings, thereby removing any unwanted compliance in the
focusing system.

**5.13.7 Scroll Shutter**

The shutter employed for the Reference Design is a scroll shutter. The
shutter will be driven in both directions and has been designed for 0.5
second maximum open or closing times (figure 5.28).

![](media/image37.wmf){width="3.907638888888889in"
height="4.5368055555555555in"}

**Figure 5.28** Scroll shutter design

The shutter has been designed as a stand-alone package that will provide
for modular installation and removal from the corrector body. The spools
will be driven by a set of Moog gear-head DC brushless motors with
encoders for position monitoring. To ensure position and exposure time
repeatability, sensors will also be mounted along the scroll track.

**5.14 Auxiliary Systems**

**5.14.1 Guiding**

The Mosaic II has two guide cameras in addition to the main image CCDs.
The guide cameras provide small corrections for the telescope tracking
system such that a star image will stay in precisely the same location
during an exposure. The Blanco accepts these signals every 2 sec. We are
currently investigating the options for guide systems. In our Reference
Design we have CCDs on the focal plane devoted to guiding along the top
and bottom of the focal plane. Corresponding slits in the shutter allow
exposure of the guide CCDs prior to exposure of the image. Figure 5.29
shows the guide CCDs in the partially vignetted region of the focal
plan. The MegaCam MMT project has a similar arrangement. Alternatively,
we are also investigating the benefits of a separate guide camera. This
type of camera could be constructed by our collaboration or purchased
elsewhere. A pickoff mirror could be installed just in front of the
shutter, outside the main image light path as shown in Figure 5.30.

![](media/image38.jpeg){width="4.9875in" height="4.6in"}

**Figure 5.29** Schematic of guide system layout with CCDs on edge of
focal plane.

![](media/image39.jpeg){width="5.4875in" height="4.65in"}

**Figure 5.30** Schematic of guide system with a pick off mirror and
separate guide camera

Guide camera CCDs have two requirements in addition to those listed in
Table 5.2. These are a fast readout rate (\<\~1sec) and the possibility
to operate in frame-store mode. The fast readout rate is needed so that
corrections to the telescope position can be generated on a time scale
that matches the 2 sec. update rate accepted by the Blanco control
system. This faster rate also implies a separate DAQ path for the guide
CCDs and this is discussed in section 5.11.

Frame-store mode is a technique for collecting clean images without the
use of a shutter. This is accomplished by coating one half of the CCD
with an opaque layer and collecting the image only in the other half. At
the end of an exposure, the charge image is transferred very quickly
(\~1ms) from the imaging side to the storage side. Once the image is on
the storage side of the CCD, it is decoupled from the imaging side and a
new exposure is initiated while the previous image is read out of the
storage area of the CCD.

The 1k × 1k LBNL CCDs described above are a good match for our guiding
requirements. To cover the desired area we would need 4 of these CCDs
(note only half of each CCD is used to collect the images). A readout
rate of 240 kpixel/sec results in a readout time of \~0.9 sec. Reading
out only a small area surrounding the guide star(s), once identified,
would greatly reduce the readout time. Figure 5.8 shows 4 guide and 4
focus CCDs along the top and bottom of the focal plane layout. There is
room for additional guide/focus CCDs if they are needed.

**5.14.2 Cloud Camera**

For accurate photometry a precise measurement of the atmospheric
conditions is needed. We will use already existing infrastructure at
Cerro Tololo (TASCA all-sky camera, Differential Image Motion Monitor
(DIMM), Multi-Aperture Scintillation Sensor (MASS), and the Weather
Station) to monitor conditions. Direct photometric monitoring using an
already-existing CTIO small telescope as an auxiliary program is a
possibility we will investigate. We may construct a cloud camera to
further monitor the sky conditions. For our reference concept we follow
the design of the cloud camera used by SDSS as shown in Figure 5.31. It
consists of an IR camera from Raytheon, a hyperbolic mirror, a control
communication system along with associated display and control software.

![](media/image40.wmf){width="3.75in" height="2.8402777777777777in"}

**Figure 5.31** SDSS Cloud Camera

**5.14.3 Full Camera Calibration System**

We are considering development of a system to calibrate the instrument
by measuring the response of the entire corrector and focal plane array
package with monochromatic light, CCD by CCD. This would involve setting
up a test beam that simulates the beam expected from the CTIO 4m
primary, a f/3 beam, illuminated by an Optronic Laboratories OL-426
(TBD) integrating sphere calibration standard. The beam will illuminate
a CCD with a calibrated surface brightness (radiance) at a given
wavelength with a small spread in wavelength. We note that the test beam
optics can be no closer than 1.5 meters from the focal plane, which
gives as a minimum size for the test beam optics of 0.5 meters. The test
beam will be able to illuminate any point on the focal plane array by
being moved by a x,y translation stage.

The integrating sphere calibration source provides a calibrated surface
brightness at the exit pupil of the sphere. This is converted to a
collimated beam using an off-axis parabolic telescope. The collimated
beam is converted to an f/3 beam by the test beam optics. This process
will produce a light on the focal plane array that is stable and whose
relative wavelength characteristics are well known, but is no longer
absolutely calibrated. If we need the absolute calibration we will
require a calibrated photodiode on the DEC focal plane edge.

The output of this test beam will be system response curves as a
function of wavelength for all the CCDs that make up the DEC focal plane
array. Given this test capability, we will also be able to measure read
noise, gain, and bad columns, and will be able to explore flat fields.
If we develop the ability to mask the sphere's exit pupil to project a
small Gaussian onto the focal plane array, we will be able to test the
optics collimation.

These tests will be of the entire Dark Energy Instrument, from focal
plane to corrector to data acquisition to observing programs. They do
not replace the test stand testing of individual CCDs.

**5.15 Activities at CTIO**

**5.15.1 Telescope Improvements (WBS 1.4)**

There are two areas in which the Blanco telescope may be usefully
improved to better support the Dark Energy Survey Project:

**5.15.1.1 The telescope control system (TCS).**

The TCS connects the operator\'s console in the control room to the
array of remotely operable systems within the telescope building. It is
normal that the TCS be upgraded from time to time and the Blanco\'s
current system is now a few years old and several subsystems are
becoming obsolete, overloaded or mildly unreliable. Whether or not the
DES instrument is deployed at the Blanco, the TCS will be upgraded, but
the arrival of the DES instrument will condition the direction taken by
this upgrade.

The general scheme of the upgraded system will follow that of the
existing system, but our intention is to migrate as close as possible to
the system used to run the new telescope SOAR which is also run by CTIO
(the TCS is LabView based). Our immediate intention is to upgrade the
SunOS Linux computer, which hosts the operator interface, and the VME
computer at the center of the system. As we progress, some of the
various subsystems of the TCS may be replaced. The upgrade will seek
maximum efficiency for the Dark Energy Survey's scheme of integration
times of 100 sec with slews of a few degrees between each.

**5.15.1.2 Image quality**

The Blanco telescope and building has, over the years, undergone a
number of modifications with the aim of reducing local atmospheric
turbulence responsible for degrading image quality (or \"seeing\").
Essentially, such turbulence degrades the image as though by convolution
with a Gaussian-like kernel of FWHM of order 1\" and variable on all
relevant timescales. This can be caused in part by poor temperature
equilibrium between the telescope and its environment. Past improvements
have involved removal of excess heat sources, relocating the control
room to the ground floor, removal of all nonessential facilities from
the telescope building, refrigeration of the dome floor, forced air
cooling of the primary mirror in daytime, and the installation of doors
in the sides of the dome to improve nighttime ventilation.

A range of possibilities remain to be explored, although it is difficult
to identify precisely what may have the greatest effect. DECam itself is
expected to improve over current prime focus image quality through the
inclusion of an active cooling system to ensure its equilibration with
ambient temperatures. We also continue to monitor and make occasional
checks of the telescope environment with a thermal infrared camera to
protect against the inadvertent introduction of new heat sources.
Further improvements are under discussion, including increasing
ventilation via additional dome doors or fans and the removal of further
unnecessary parts of the telescope environment (including, possibly, the
wholesale removal of nonessential walls). Nevertheless, it must be
remembered that we are ultimately limited by the heavy steel and
concrete construction of the telescope and building.

Image quality is also affected by the optical quality of the telescope.
The Blanco does incorporate active support of the primary, although it
is only possible to apply small correction of low order aberrations in a
50 cm thick Cervit mirror. This active control system is tuned up
occasionally and future in-depth tests specific to the prime focus are
planned. Again, DECam itself is expected to improve the quality of
Blanco observations via a better corrector than that which is currently
installed.

**5.15.2 Commissioning at CTIO (WBS 1.5)**

Here we summarize the plans for commissioning of the Dark Survey
instrument at CTIO. On delivery to CTIO, we anticipate a period of
commissioning before the instrument is ready to reliably produce
scientific data. The stages of this process and estimates of the time
investment required are as follows:

-   Delivery.

-   Unpacking (1d).

-   Assembly of dewar (2d).

-   Vacuum integrity verification (without mosaic installed) (3d).

-   Assembly of mosaic and dewar (2d).

-   Pump assembled dewar until specified vacuum quality achieved (5d).

-   Mosaic brought to operating temperature (2d).

-   Assembly of controller (1d).

-   Verification of controller integrity (2d).

-   Controller and mosaic connected (1d).

-   Mosaic read out and verified (3d).

-   Assembly of corrector (2d).

-   Assembly and test of filter, focus, shutter and f/8 mechanisms (4d).

-   Assembly of prime focus cage (1d).

-   Integration of corrector, dewar, filter mechanism, focus mechanism,
    shutter

mechanism, controller and prime focus cage (5d).

-   Test of integrated prime focus assembly mechanisms (3d).

-   Reference optical alignment of prime focus assembly (3d).

Some of these items can proceed in parallel. We estimate that it will
take a total of 6 weeks to complete the reassembly and testing of all
the components in the prime focus cage. We would then proceed with the
operations that will impact telescope operations:

-   f/8 mirror installed (1d).

-   Telescope taken off line.

-   Old prime focus assembly removed (1d).

-   New prime focus assembly installed (1d).

-   Balance the telescope (1d).

-   Integration of DECam with Blanco TCS (3d).

-   *In situ* verification of prime focus assembly mechanisms (2d).

-   Reference, coarse alignment of prime focus cage (2d).

-   Fine alignment of prime focus corrector and mosaic (2d).

-   Fine alignment of f/8 secondary (1d).

-   *In situ* characterization of DECam CCDs (3d).

-   First light.

-   Hartmann testing of DECam (1d).

-   Shack-Hartmann testing of f/8 (1d).

-   Fine tuning of optical system (1d).

-   Photometric characterization of DECam on standard stars (4d).

-   Astrometric characterization of DECam (4d).

At this point (12 weeks after delivery to CTIO) we are ready to take
images and begin to commission the operation of the Dark Energy Survey
Instrument. All software associated with instrument control, readout,
and data handling will be fully tested and debugged before delivery. We
estimate that this commissioning period will take 4 weeks.

**5.16 System Integration (WBS 1.6)**

The system integration task is largely one of document generation,
maintenance and distribution, and control functions to verify
compliance. The goal is to assure that the pieces that comprise the Dark
Energy project fit together properly and work in harmony.

**5.16.1 System Integration Organization**

The integration of the DES instrument falls into two major
categories: 1. the integration of the physical elements that are
assembled into the cage, and 2. the integration of the cage and its
internal elements with the telescope. The intention is to assemble the
cage and test it at Fermilab, and ship it either as one piece or in a
few large pieces to Cerro-Tololo for final assembly on the telescope.
Each of the major subsystems that comprise the cage has its own
engineer, who may have a particular specialty and multiple
responsibilities. Out of this group, we will construct an Integration
Committee that will advise the System Integration Leader and the Project
Manager. After the project is baselined, changes required for
integration issues will be passed up through the change control
mechanism.

1\. Project Manager \-\-\-\-\-\-\-\-\-\-\-\-\-\-\-- Change Control

1.6 System Integration

Integration Committee

Document control

Document distribution

1.6.1 Cage Integration 1.6.2 Telescope Integration

1.6.1.1 Mechanical 1.6.2.1 Mechanical

1.6.1.2 Electrical 1.6.2.2 Electrical

1.6.1.3 Cooling 1.6.2.3 Cryogenic & cooling systems

1.6.1.4 Optical 1.6.2.4 Telescope improvements

1.6.1.5 Controls 1.6.2.5 Telescope environment, power,

etc.

This particular organization covers the major functions that have to be
integrated as we see the task now. It will evolve as we gain experience.

**5.17 Summary**

We have described a Reference Design for the Dark Energy Survey
Instrument that meets our technical specifications. The Reference Design
represents our current choices

for the Dark Survey Instrument design and construction. However, further
analysis may show that better, more cost effective options are
available. Estimates of the corresponding cost and schedule for the
Reference Design will be discussed in Chapter 7.

**References**

1)  S.E. Holland, D.E. Groom, N.P. Palaio, R. J. Stover, and M. Wei,
    \"Fully\
    Depleted, Back-Illuminated Charge-Coupled Devices Fabricated on\
    High-Resistivity Silicon,\" IEEE Trans. Electron Dev. 50 (3),
    225\--238\
    (January 2003)

2)  Plot take from "An Assessment of the Optical Detector Systems for
    the W.M. Keck Observatory," by J. Beletic, R. Stover, and K. Taylor
    (January 2001).

3)  J.P. Walder, et al, \"A Low-Power, Wide Dynamic Range, Multi-Gain
    Signal\
    Processor for the SNAP CCD \" to be published in Proceedings of the
    Nuclear Science Symposium, October 2003.

4)  To be published in SPIE, H. Oluseyi et al, "LBNL 4-side Buttable CCD
    Package Development".

5)  Monsoon CCD readout system developed by NOAO.

**6. Data Management**

The Dark Energy Survey data management component will handle the data
from the moment it is written by the data acquisition system onto a disk
sitting on Cerro Tololo to the time when science and public archive
ready data products are available in Illinois. The goal is to develop a
series of automated pipelines that produce high quality data products
with minimal human intervention. The data management effort will include
a significant simulation effort to verify the pipelines during the
construction phase and continually test the pipelines during the survey
phase. Data quality assurance through autonomous processing, human
visualization, and ultimately scientific analyses by the DES
collaboration will lead to robust, science-ready data for the whole
community. A philosophy that underlies this data management solution is
to use existing, hardened code for the bulk of the reduction pipeline.
Because large CCD imagers have been around for some time and are used
widely in the community, we can draw upon a wealth of existing
solutions. The primary challenge will be in scaling these solutions to
an order of magnitude higher data rate and creating a seamless,
overarching structure that coordinates the observing, reductions,
archiving, public access and science analyses. The lessons we learn will
feed the solutions being designed and developed for the Large Synoptic
Survey Telescope, which is a far more challenging problem.

The survey will be organized to deliver constraints on the dark energy
in concert with the South Pole Telescope (SPT) cluster survey after the
second observing season. Science return from the other key projects (SN
survey, weak lensing and galaxy angular power spectrum evolution) will
also begin after the second observing season, with analyses of the full
datasets available within one year of the completion of the survey. The
public data releases will be organized into two efforts: (1) raw and
fully reduced, astrometrically calibrated single-pointing images with
the best available photometric calibration will be released to the
public 1 year after they are acquired; (2) co-added images that are
being built up in four bands together with associated object catalogs
with photometry and astrometry to specified accuracy will be released
once at the midpoint of the survey and then again one year after the
completion of the survey.

The survey data management will be a collaborative endeavor led by the
University of Illinois (U Illinois) and involving chiefly Fermilab and
NOAO but also with possible contributions from the University of Chicago
and LBNL. U Illinois and Fermilab will provide the bulk of both the
personnel and the hardware resources necessary to support the data
management. Our data plan must address not only the technical issues of
data handling across the sites, but also the issues of distributed
software and system development. Our data plan aims to leverage
efficiently the expertise and resources from the participating sites. In
this chapter we describe a collaborative approach to meeting our data
management goals. We also address the use of the tools developed for
survey data management for non-survey observations, such as those
obtained during time generally allocated by NOAO to its user community.

## The Development Process

The first step toward delivering the data management system is a design
phase, which is already underway. The main deliverables of this phase
will be:

-   Detailed requirements for a data management system necessary which
    meets the

science requirements

-   A high-level architecture design

-   A set of work packages---development modules that constitute the
    deliverable

system components.

-   A software development process

-   A reference software environment

-   Coding guidelines

Within Chapter 7 ("Project Management Plan"), we discuss how the
collaboration will deploy its personnel to deliver these design
components. In this chapter, we discuss the technical side of the design
as it has been developed thus far. In particular, it is worth discussing
how the development process will guide design and implementation.

The software process is the set of policies that we will follow to
support the distributed development of the system. The process should be
lightweight enough to allow sufficiently rapid development. The
development process will include a design phase for each work package
containing multiple stages, each capturing increasing detail (e.g.
"conceptual", "preliminary," and "comprehensive"); these would be
reviewed by the development team across the collaboration to capture
overlap and interfaces between packages early in the process.

An important part of the software process is the definition of the
*reference software environment*. This is the environment necessary to
develop and run the software. It includes the target hardware platform
types (e.g. clusters, single-node machines), operating systems, allowed
computer languages, compiler versions, and necessary libraries. All
software will be required to run in this reference environment. The
reference environment should not be so restrictive as to make it
impractical to run the software except on a few specific computers (the
exception, perhaps, being hardware interface software), nor so
permissive that we support unused platforms. At a minimum it should
include the platforms that we expect to use during the operation phase.
If the reference environment is simple to assemble, the
code---particularly the pipeline software---will be more portable to
other sites.

Informing the review process should be a set of coding guidelines.
Usually language-specific, these define minimum coding practices that
allow for collaborative maintenance of the code and efficient
integration. The documentation practices form an important part of the
coding guidelines. Meeting these guidelines will be required for the
code to be considered complete and accepted by the project. Another
important component are any requirements for the development of
associated test code for the various software components.

Finally, we will deploy a software repository, building, and testing
environment. All sites will have access to the code via CVS (or some
other appropriate network-aware revision control system). The build
system will "check-out" the code from the repository periodically and
run a battery of test suites. Thus, a testing framework will be needed
to enable automatic testing which can catch inadvertent "breakage" of
the code throughout the build phase.

We expect to borrow heavily from the Sloan project at Fermilab and the
CARMA Computing group at NCSA to deploy the software process. In
particular, it would be straight-forward to reuse many of the
technologies these groups use to manage their software processes,
technologies like Bugzilla for error tracking, JUnit for testing, and
*make*-based build systems.

## Data Products and Data Storage

In the current camera design, the focal plane is tiled with sixty-two
2048x4096 science devices, and the ADC will deliver data with 16 bits
per pixel. Each detector will have 2 readout amplifiers, and a full
single-pointing image will be 1.0GB. Our current plan is for a single
read of the DECam to produce 128 2048x2048 FITS images. We will organize
each of these reads into a directory with a naming convention and header
keywords that specify the readout amplifier and detector of origin. The
raw data products coming from the telescope will be images with
populated header information from the CCDs in the camera after exposure;
these constitute level 0 products. We will produce roughly 100 TB of raw
imaging data over the course of the survey. These must be calibrated to
remove instrumental response. The individual calibrated images
themselves will be of value to the astronomical community as the basis
for research that lies outside the scope of our science goals; thus,
they can be released as a "level 1" product.

The primary product that will form the foundation for the survey
science---the level 2 product---will be a set of four image collection,
one for each band, formed from the co-addition/mosaicing of all the data
from the survey. The data will likely be stored (and made available) as
regular tiles that share a single image scale calibration, covering the
entire survey region; however, access to arbitrary mosaiced cut-outs
will be made available. From the co-added images, we will produce a set
of higher level (level 3) catalog products. Most notable of these
catalogs will be a full object catalog listing photometry and positions
for all detected objects in the survey.

The highest level products---level 4---are those produced by the science
teams and form the basis of scientific analysis. Among these are the
photometric-redshift catalog (containing photometry and redshifts for
all galaxies detected), and the galaxy cluster catalog. The practical
distinction between level 3 and level 4 products is in the applicable
public release policy (described in section 6.4): level 3 products will
be released to the public along with the co-added images, while release
of level 4 products will be released by the time of the science
publication.

Experience from the SDSS shows that data processing actually expands the
data collection rather than reducing it. We estimate that after the
first year we will need to house roughly 100 TB of raw and reduced data
products. By the end of the project, the total storage needs will
approach 1 Petabyte, though much of this can reside in secondary storage
(i.e. tape).

## System and Software Architecture

In this section, we outline the system and software architecture
necessary to create and support the necessary science data products.
Figure 6.1 illustrates the overall data flow. All survey data
products---raw and processed---will be archived at NCSA. Our model
allows some or all of the archive to be replicated at Fermilab or
elsewhere in the collaboration. Similarly, while we expect that the NCSA
processing resources (see section 6.5.5) will be sufficient for
production processing, we adopt a grid-based architecture to allow
processing to be shared with Fermilab platforms, as well as the wider
grid community. This will be important when significant reprocessing is
needed (we are baselining a complete reprocessing of the dataset after
each observing season). Our initial requirement for grid architectures
is aimed to support simulations that will be produced at Fermilab and
analyzed at NCSA.

We see the pipeline as being made up of two layers. The "science" layer
contains the software modules that contain the science algorithms to be
applied to the data. The "data management" layer handles the execution
of those modules as well as the management of the input and output data
(i.e. transferring them from storage to the compute platform and back)
within some execution environment. By separating data management and
science software modules, the modules will be more easily adaptable to
different execution environments.

In both layers, we would like to maximize the amount of software reuse.
In the data management layer, we propose to build a framework from
existing grid-based solutions. The major virtue of a grid-based
architecture is its transparent access to computing and storage
resources, regardless of the specific platforms being used. Not only
will this lower the cost of developing the framework, it will be
critical to leveraging the resources at both NCSA and Fermilab in a
uniform way. We plan to take advantage of our previous experience with
these technologies from the TeraGrid and the Open Science Grid
Initiative to rapidly define our grid-based environment. In the science
domain, we will draw from the existing, well-tested suites of
astronomical software, only writing new software where the science
demands a new implementation. This means that our software framework
must support the execution environments of one---or possibly several---
external packages.

## 6.4 Public Data Release and Pipeline Access {#public-data-release-and-pipeline-access .unnumbered}

A public archive of the survey data will be maintained by NOAO and NCSA.
This public archive will include raw and reduced (level 0 and level 1
data), single pointing images with astrometric and photometric
calibration with uncertainties. The default photometric calibration will
be carried out using USNO-B2 catalogs, which will provide a stable
source of calibration information from the beginning of the survey. Over
time we will build up directly calibrated DES imaging over the survey
region; once we develop a robust calibration, we plan to transition away
from USNO-B2 to an internal DES calibration. Our goal is to make this
transition by the midpoint of the survey, when our first catalog and
co-add data release occurs (see below). These reduced images will be
made available to the public 1 year after they are acquired. Full
reprocessing with improved algorithms will necessarily include some data
that has already been released. These data will be replaced in the
archive with updated header information to indicate the changes. In
addition to these imaging-only automated releases, we expect to release
the coadded images in each band and associated catalog information
(astrometry, photometry, star-galaxy classification, etc) at the
midpoint of the survey in 2011 and again one year after the end of the
last observing season.

In addition, we plan to allow full access to the pipelines (and the
pipeline software) that produce these single-pointing images and the
coadded images. Cluster catalogs, shear maps, photometric redshift
estimates and other derived properties will be released upon publication
of the results and at the discretion of the individual science teams.
These teams may choose to coordinate release of some products with the
release of the level 3 products.

## Overview of Data Management Operations

Following the development phase (2004-2008), the data management
component will enter the operations phase (2009-2014). Naturally, the
development is driven by the operational requirements of the data
management component, and so we first describe the operations phase in
some detail.

To estimate the nightly data production, we adopt an 8 hour observing
session with an imaging cadence of 2 minutes. This corresponds to about
240 science images per night. To this we add 60 calibration images
(assumed to be 10 flats in each filter, 10 bias frames and 10 dark
frames) to produce a total of 300 images or 300GB of raw data for the
night. If we include a 30% data compression gain, then during the
science observing, there is a sustained data rate of 0.4GB/min or
54Mbps. Add to this a 20% calibration overhead if the goal is to
transfer the calibration data by the end of the night. It is likely that
the raw calibration data can be transferred during the day following the
observing, and only the final, reduced flats and bias frames are
transferred to La Serena at the beginning of the night to facilitate the
time domain reductions.

## CTIO Operations

The DECam data acquisition system will deposit imaging data onto a disk
storage system. We will install an "instrument cache" to capture all
data flowing from the instrument. This will provide both a buffer for
extremely rapid data quality analysis on a subset of the data and a
queuing area for the data to be transported to La Serena. Nominally this
would be a 4TB RAID "data brick" (\$9K today), which would provide safe
storage for two weeks of observations or storage for shorter periods
together with storage of calibrations and other standard files
(catalogs, etc.) needed for and produced by the data quality
comparisons. Data would be deleted from this mountain cache only when
transfer to both La Serena and U Illinois is verified.

In general, we want to keep the mountain computing activities to a
minimum, because (a) a viable link to La Serena will exist and (b)
computing resources can be more easily maintained in La Serena. However,
we have to expect failures in the link from time to time, and so key
quality assurance processing must be carried out on the mountain. A fast
workstation will be positioned on the mountain and used to process a
subset of the data coming from the camera. This subset could be as few
as 5 or 6 of the imaging nodes. These data would be fully
reduced---including an astrometric and rough photometric calibration.
This rough photometric calibration could come from either (a) the
available USNO B2 stellar plate photometry, (b) the photometric solution
derived from standard star observations that night, or (c) overlap with
other calibrated DES imaging data. Image statistics such as the PSF and
its variation, the sky brightness, the extinction, the estimated 10
sigma stellar photometric limit, and the number of bad or saturated
pixels will be recorded in the survey database. A telescope pointing
correction will be calculated, and the operator will be given the option
of adjusting the pointing automatically. This same system will allow
data visualization by the observer, and it could be used to produce the
nightly calibration products to be sent to La Serena for the time domain
analysis.

## Data Transfer to La Serena

Assuming that raw calibration data will be sent post observing, the
bandwidth requirements on the CTIO-La Serena link are driven by the need
to transfer one science image before the next one is produced. Our
minimum bandwidth needed for normal DES operations is then 1GB in 2 min
= 54Mbps (assuming 30% compression). Including some contingency for
overheads and other necessary network traffic (such as frequent video
links between observers and survey scientists in Illinois), we should
aim to have \~100Mbps available for normal survey operations.

The current CTIO-La Serena network connectivity is provided by a direct
microwave link with 99.9% uptime design specifications which is shared
between CTIO, SOAR, and Gemini. The current bandwidth available to
CTIO+SOAR (not including Gemini) is 155Mbps over one channel on the
microwave link. Gemini operates over a separate 155Mbps channel, and the
two channels provide backup/fail over for each other. Future network
infrastructure could include an additional 155Mbps channel (the quantum
for upgrades to the current link), which would cost \~\$50K today. If
LSST is sited on Cerro Pachon, it is likely that NOAO and LSST will
install a fiber link directly from La Serena to Cerro Pachon and Cerro
Tololo. It may be possible to install this well before LSST's expected
first light, possibly even before the commencement of DES survey
operations. Such a fiber link would allow almost unlimited bandwidth for
use by DECam and other high-data-rate instruments coming to Cerro Tololo
and Cerro Pachon.

Although we would have room to cache two weeks of data on the mountain,
in the unlikely event of a failure in the mountain to La Serena link, we
would like to continue to move the data at a relatively normal cadence.
This would be required to ensure full quality assurance and timely
processing for time domain analysis. In the case of failure, we would
use "sneakernet", shipping the data on disks down from the mountain and
possibly all the way to U Illinois. This disk-based transfer (if done
today) would be based on 1TB firewire/USB 2.0 drives, which could store
three nights of raw data. We might plan to have three to five 1TB boxes
(\$1.2K each today) on the mountain, ready for use in the event of such
a failure.

## La Serena Operations

Data processing at La Serena will focus on the SN alert processing.
Roughly 10% of the survey time will be used for repeat observations of
fields to find and follow SNe. Given the time sensitive nature of the
data analysis and SN discovery, the main processing center for this
component of the survey data will be in La Serena. DES/SN data will be
analyzed for transients, and SN candidates will be automatically flagged
for external followup (such as spectroscopy) and filed to a publicly
accessible database. Candidates will be analyzed for color and light
curve shape on the fly, which will facilitate the SN type and distance
estimates, providing necessary information for prioritization of the
candidates. This processing should be rapid enough that candidates are
made available to the community before the next observing night. The
processing infrastructure should be modular and scalable, allowing for
the addition of processing modules. An existing solution to process the
SN survey data, the SuperMACHO/ESSENCE projects' SM/SN pipeline, is
currently in operation at CTIO. This pipeline handles 20 GB of raw CTIO
4m MOSAIC data (this camera is 7.5times smaller than DECam) in a night.
The DES/SN component will deliver data at a rate of \~30GB/night, not
far beyond the current data rate handled by the SM/SN computing cluster
(20 1Ghz class CPUs). Thus, we expect the natural increase in CPU speeds
between now and 2009 will enable a cluster of moderate scale to carry
out these reductions.

## Data Transfer to Illinois

We plan to use international network connectivity to automatically
transfer the data from La Serena to the NCSA archive at U Illinois. Our
goal is to completely transfer each night of data in under 24 hours. One
night of data is about 300GB, so we need a sustained transfer rate of at
least 21Mbps for the DES, assuming that we transfer only raw data and
achieve 30% compression. Note that this assumes we do not bother
transferring anything besides the raw data. To allow for contingency and
overheads in the transfer, we require a bandwidth of 50Mbps for DES
operations. Currently there is a 10 Mbps link directly from La Serena to
Miami, where we access Internet2. This 10 Mbps is broken up "4/6/10",
where NOAO is guaranteed 4 Mbps, Gemini 6 Mbps, but either can burst up
to the full 10Mbps. This link will definitely need to be upgraded in
order to provide effective data transfer of DES data to NCSA. NOAO is
currently actively pursuing a variety of avenues to provide a
significant upgrade to its current international bandwidth. These
efforts are being undertaken in collaboration with Gemini and other
Chilean-based astronomical facilities. It is likely that the bandwidth
will grow by 2009, the first year of observing for DES, and we will work
with NOAO to make sure this happens.

This international connectivity will almost certainly not be as reliable
as the connection from telescope down to La Serena, and therefore a
backup data transfer strategy is an even higher priority. Although the
transport time is significantly longer, the same "sneakernet" solution
described above, regularly shipping TB disks, will be used in the case
of extended network outages and/or when the network transfer gets
significantly behind the real time data flow. Given that the NCSA data
processing is not as time critical, the delay of approximately one week
for international shipping should not pose a significant problem for the
survey. It is however necessary to scale the NCSA (and/or grid)
processing capability to be able to "catch up" with any significant
delay by processing more than one night's data per day.

## U Illinois Operations

U Illinois operations will largely rely on NCSA computing resources for
archiving and processing the DES data. The goals are to (a) produce
calibrated single pointing images, (b) produce coadded images in each of
the four bands, (c) produce object catalogs that will feed the science
analysis, (d) provide for seamless collaboration and public access to
the data, and (e) process the data at a rate faster than it is being
produced. In addition, we expect to reprocess all the data from time to
time as improvements in the algorithms and better calibration products
become available. These goals require a large data storage facility
approaching 500TB, modest real-time processing capabilities, and the
development of grid-based processing algorithms to speed the
reprocessing of the data. Below we describe each of these components in
more detail.

NCSA has large amounts of storage available today, and there are plans
to increase this capacity by more than an order of magnitude on the
timescale of the DES. The NCSA director will provide 100TB of spinning
disk storage together with mass tape storage of 400TB to support the DES
reductions and archiving (see attached letter of support). All raw data
will be stored, and processed data products like single-pointing images,
coadded images and object catalogs will also be stored.

The single image processing pipeline will be the same as the one
deployed for the SN alert processing in La Serena. The single pointing
image will consist of 128 individual FITS images (one for each
amplifier). Images will be bias and dark subtracted, flat fielded,
fringe corrected, cross-talk corrected and pupil ghost corrected. An
astrometric solution will be determined and WCS parameters will be
stored in the header. Photometric calibration will be performed using
calibration data from the night of observation, indirect calibration
data from previously obtained directly calibrated DES data, or indirect
calibration data from existing plate photometry catalogs like USNO B2.
The photometric zeropoint and uncertainty will be stored in the image
header. As noted previously, the modules required for this analysis
exist in a variety of packages currently being used to reduce similar
data on a smaller scale.

The full dataset will be probed for time domain purposes during the
processing at NCSA. Varying sources of value to the community will be
flagged and released to the community through the system set up to
support the DES/SN time domain science at CTIO.

The fully reduced single-pointing images will be combined or co-added
into deeper images. This process will enhance the uniformity of the data
(removing blank spots caused by chip gaps, reducing PSF variations) and
will allow us to push to the depths required to obtain cluster
photometric redshifts out to z\~1.3. There are existing solutions for
this pipeline as well, and we will be examining these to see how they
perform under seeing variations and image depth variations. We will
construct a pipeline that will deliver accurate star galaxy separation
and galaxy photometry based on the DES data.

Cataloging will be carried out at many points in the data management.
Object lists will be needed for the astrometric and photometric
calibration of the single-pointing images. Time domain astronomy
requires comparison of photometry in multi-epoch single frame images.
Science-focused catalogues will be derived primarily from the co-added
images (except for time domain science), because faint object detection,
object characterization and corruption caused by edge effects will be
less of a problem in these data.

There are many available cataloging programs, and some have seen
extensive use. We plan to explore a range of cataloguers, but our
strawman application is SExtractor, written by Emanuel Bertin. This
cataloguer is very fast, produces simple ASCII object lists, and we have
experience using it to do science.

Data reprocessing is inevitable. We will plan to reprocess all the data
during the periods March-August, between every observing season. This
may not be necessary, but we will adopt this processing in our strawman
design. We will develop and apply grid based processing algorithms for
this reprocessing, which will significantly speed the process.
Single-pointing processing can be data-parallelized at the level of one
night's data trivially, and further partitioning of the data is also
possible. The DES will serve as a challenging test application for the
computer science community at NCSA, Fermilab and elsewhere as they
develop the next generation tools.

## Fermilab Operations

Computing facilities at Fermilab, managed on behalf of the DES project
by the Experimental Astrophysics Group and the Computing Division, will
be configured as processing node in our deployed pipeline framework.
Part or all of the archive will be replicated there as well, allowing us
to leverage resources there for processing as needed. While we expect
that much of the day-to-day processing will be carried out using NCSA
facilities, we expect to make heavy use of Fermilab resources during
bulk reprocessing. We also expect to use Fermilab facilities in part to
support the generation of some level 4 products and the science analysis
of those products.

## Science Operations

Science operations will be underway at all five collaboration sites. In
addition to the primary data processing, time domain and simulation
pipelines described above, there will be a series of special purpose,
science focused pipelines developed to facilitate the key science
program, a study of the dark energy using (a) cluster surveys, (b) weak
lensing studies, (c) galaxy angular power spectrum evolution and (d) SNe
Ia distance estimates. The DES science operations and data management
are separate components of the project, but close coordination and broad
avenues for feedback are required for the efforts to succeed. The data
management will facilitate the science operations, and the science
operations will be a key data quality verification tool for the DES.

## General User DECam Data Management

While we have focused on the data management for the Dark Energy Survey
in the previous sections, we recognize that the DECam instrument will
also be commonly used as a facility instrument for observers granted
time through the normal NOAO time allocation process. Indeed, as we
outline in Chapter 9, there are many exciting science cases for use of
this wide-field instrument on the Blanco 4m which lie well outside the
DES science case. Given the size of DECam images, pursuit of those and
other observations by NOAO users will require significant resources to
transform the raw data into data products ready for scientific analysis.

Fortunately, the data reduction requirements for almost any scientific
project using the DECcam instrument will share the general requirements
of most of the DES processing steps described above, at least through
production of level 2 and possibly level 3 products (stacked combined
images and catalogs, respectively). Thus, while the DES collaboration
itself cannot take responsibility for actually delivering such reduced
data products to all users, the collaboration will work closely with the
NOAO Data Products Program to ensure that the software can be run on
general user data to provide level 1, 2, and 3 data products for NOAO
users and the NOAO Science Archive.

Indeed, a significant portion of the DES pipeline and infrastructure
will be installed and run on-site at the La Serena facilities in support
of the DES/SN program.Working with the DES, the NOAO Data Products
Program can build uponthis foundation of both hardware and software to
provide general users of the DECam with reduced data products on
timescales similar to those required for the SN analysis, usually within
less than 24 hours, if not in near-real time. The rapid availability of
such high quality data products would not only make use of the DECam
feasible for general users, it could also open new opportunities for
exciting scientific programs which require rapid analysis for the
planning of observations on subsequent nights, either on the DECam or
other followup telescope facilities.

The archiving both raw and reduced data products from general DECam use
will be the responsibility of the NOAO Data Products Program. However,
it should be noted that NOAO and NCSA are currently developing a close
data management collaboration covering the transport, storage,
reduction, and retrieval of current and future NOAO data products
through advanced archives, portals, and the VO. It is therefore highly
likely that the final processing and archiving of the data obtained by
general NOAO users will be done in collaboration with NCSA, using much
of the same infrastructure (both hardware and software) used in support
of the DES.

## Work Packages

As will be described in Chapter 7, we will manage our software
development through a well planned work breakdown structure (WBS)---a
hierarchical organization of tasks necessary to complete and operate the
data management system. Work packages, which form the second level of
our WBS, represent a logical component of the system that should be
designed and implemented as an integrated unit. In this section, we
summarize each of the work packages necessary to enable the operations
described in the previous section. These packages are numbered according
to the project WBS outlined and displayed in Ch. 7. While the focus of
this section is on the purpose of each package and the expected approach
to design, estimated person-power costs are also given (in units of
FTE-months). We revisit these packages and cost in the next chapter
where we discuss costs and schedule.

![](media/image41.wmf){width="5.995833333333334in"
height="3.3361111111111112in"}

Figure 6.1. Dark Energy Survey Data Management Work Breakdown Structure

### WBS 2.1 Management and Software Engineering (20 total FTE-months) {#wbs-2.1-management-and-software-engineering-20-total-fte-months .Mnormal}

### WBS 2.1.1. Project Management and Collaboration (FTE-months: 12.0[^13]) {#wbs-2.1.1.-project-management-and-collaboration-fte-months-12.0 .Mnormal}

### This package covers time and effort by the Project Manager necessary to manage and track the development of the software. Each developer will spend time participating in design reviews and regular telecons.  {#this-package-covers-time-and-effort-by-the-project-manager-necessary-to-manage-and-track-the-development-of-the-software.-each-developer-will-spend-time-participating-in-design-reviews-and-regular-telecons. .Mnormal}

### WBS 2.1.2 Software Development Framework: (FTE-months: 6)  {#wbs-2.1.2-software-development-framework-fte-months-6 .Mnormal}

### This package covers the software that supports our data management development. It includes an automated system for building and testing the software (apart from the simulation testbed). It also includes work that might come with managing code review activities. It may also include software for packaging our software and delivering to the processing platforms. This package will depend critically on the high-level choices to be made by the DMSC, including the definition of a software design and review process and the design reference platform, discussed in section 6.1.  {#this-package-covers-the-software-that-supports-our-data-management-development.-it-includes-an-automated-system-for-building-and-testing-the-software-apart-from-the-simulation-testbed.-it-also-includes-work-that-might-come-with-managing-code-review-activities.-it-may-also-include-software-for-packaging-our-software-and-delivering-to-the-processing-platforms.-this-package-will-depend-critically-on-the-high-level-choices-to-be-made-by-the-dmsc-including-the-definition-of-a-software-design-and-review-process-and-the-design-reference-platform-discussed-in-section-6.1. .Mnormal}

The build system can be built on GNU make or Apache Ant, drawing on the
experience within our group developing and using build systems that
operate in an distributed environment. Tinderbox (used by CARMA) can be
used to monitor and test automated builds.

#### WBS 2.1.3 Software Repository: (FTE-months: 1 months)  {#wbs-2.1.3-software-repository-fte-months-1-months .unnumbered}

This package concerns the setting up and maintenance of a shared
repository for software. This repository will have two parts to it. The
tools half will house third party software, imported for reuse in the
DES DM system. The other half will come in the form of a CVS archive
maintained at NCSA with remote access enabled; this archive will hold
all software that is written specifically for this project. This package
includes the initial design and setup as well as on-going maintenance.

### WBS 2.2 Data Acquisition and Transfer (20 total FTE-months) {#wbs-2.2-data-acquisition-and-transfer-20-total-fte-months .unnumbered}

### WBS 2.2.1 Scheduling and Observing Tools: (FTE-months: 12)  {#wbs-2.2.1-scheduling-and-observing-tools-fte-months-12 .unnumbered}

This program will include a GUI that serves as the observer's primary
interface with the camera and the telescope. The tool will be an expert
system of sorts to remove the vagaries of observing and observers from
the long term survey. Interaction with the survey database will be
critical, as this program determines the observing sequence and allows
inputs from the observer on any changes to that program. For example, on
a typical night this program will select an observing strategy given the
seeing, transparency, lunar phase, and date. This interface will drive
the transition from filter to filter at a single pointing (using either
the dark time set *g*/*r* or the bright time set *i*/*z*) or send
commands to the telescope for short slew to the next pointing. The tool
will send readout commands to the camera guider devices, using expected
positions of guide stars of interest to select subchip regions. In
addition, the tool will trigger data quality calculations on each frame
that comes off the camera, and use the results to adjust the observing
plan.

A variety of observing modes will have to be supported, such as: (a)
main survey mode, (b) SNe time domain sequences, (c) dither patterns for
deep exposures in a single field, (d) dome flats, (e) sky flats, (f)
dark and bias frames, (g) photometric calibration sequences, and (g)
simple point and shoot. This range of modes should provide for efficient
observing both for survey operations and for non-survey, visiting
astronomer use.

This program will automatically display some components of each frame
coming from DECam as well as a range of image statistics. As part of the
data quality assessment carried out on each frame, an astrometric
solution will be calculated. This will enable automated updating of the
telescope pointing, and over time these updates can be tracked and
folded into an improved pointing model for the telescope.

There will have to be a monitoring component of this program that keeps
watch on a wide range of important camera, telescope, data and weather
issues that together specify the observing environment for the night and
impact the data quality. For example, detector temperatures and LN2
levels within the dewar should be monitored. Seeing from the seeing
monitor, seeing from direct measurements on the DECam frames and
transparency should be monitored. Disk space on the data acquisition
computer and the progress of data transfer to La Serena and NCSA should
be monitored. The observer will be alerted when any monitored quantity
begins to fall outside a predefined allowed range or begins to trend
toward one of these boundaries. We will draw upon experience at Fermilab
with a similar system used in the SDSS.

Tools with much of the functionality described above already exist at
CTIO and other observatories. The freshest component of this tool is its
expert capability in planning a night of survey observing, given the
state of the data already taken and the observing conditions. Efficient
interaction with a survey database, where data quality for each image is
stored, will be critical. Development of this tool must involve CTIO
telescope experts led by Tim Abbott, the data acquisition team led by
Jon Thaler, the survey strategy team led by Jim Annis as well as the
data management team.

#### WBS 2.2.2 Archiving and Data Transfer: (FTE-months: 4)  {#wbs-2.2.2-archiving-and-data-transfer-fte-months-4 .unnumbered}

The goal of this package is to accept the raw data from the data
acquisition (DA) system, deliver it to the time-critical processing
center in La Serena, and from there deliver it to the long-term archive
at NCSA. This package may include some facilities that aid the DA in
producing proper level 0 data products; for example, it may provide an
interface that allows the DA to include scheduling related metadata in
the output FITS files. It may also include software for managing the
on-site and La Serena data caches. An automated system for data
mirroring will continually transfer data from the telescope site through
La Serena to NCSA. At NCSA, a data ingest module will be necessary for
"ingesting" new data products into the archive: this includes extracting
metadata from the datasets for loading into an observation database as
well as organizing and packaging the data files for long term storage.

The raw data---i.e. the level 0 products---will be stored in the archive
as FITS files, ensuring they will be readable well beyond the supported
life of the project. To avoid any data transformations---and thus
opportunity for data corruption---before the raw data is archived, the
DA system should write the data directly into FITS files. Standard
community code (FITSIO) will be used to read and write this data. Data
cache management and data mirroring will leverage a general purpose
system being developed jointly by NOAO and NCSA; this work in turn is
leveraging existing software currently in use by these partners
(including the Storage Resource Broker, SRB; the queue-based image
caching system used at NOAO, STB; the open source mirroring tool, rsync;
the rsync-based data transfer system for the BIMA telescope, the data
cache management system used by the BIMA Data Archive, and
Fermilab/Jefferson Lab/LBNL Storage Resource Management SRM). Data
ingest operations tend to be highly project-specific; thus, there is
less opportunity for reusing software for this task.

#### WBS 2.2.3 Automated Quality Assessment: (FTE-months: 4)  {#wbs-2.2.3-automated-quality-assessment-fte-months-4 .unnumbered}

Tools to automatically evaluate the data quality will be critical to the
success of the Dark Energy Survey. We will deploy these tools at the
telescope to assess a fraction of the imaging data (\~10%) from each
exposure. We could deploy these tools in La Serena to fully characterize
the data coming from the mountain, and we will deploy the same tools at
NCSA and elsewhere, where full scale data processing is carried out.
Specific characteristics of interest include: (a) PSF statistics and
variations over the field, (b) sky brightness and variations, (c)
transparency and photometric zeropoint estimates, (d) number of bad
pixels or saturated pixels, (e) bias levels and their variations, (f)
number of detected stars and galaxies, and (g) perhaps even object by
object comparisons within some magnitude range to previously obtained
DES frames (or the USNO-B2 catalog when no DES data are available).
Object by object comparisons could examine the scatter in the photometry
as well as the star/galaxy classification.

The tools required for automated quality assurance largely hinge on a
robust cataloguer (i.e. SExtractor), automated astrometric solutions
(required as a standard part of the processing) and efficient
correlation of catalogs. As our experience with the DECam improves, we
will undoubtedly want to build in new data quality tests.

### WBS 2.3 Archive-based Data Access: (12 total FTE-months) {#wbs-2.3-archive-based-data-access-12-total-fte-months .unnumbered}

#### WBS 2.3.1 Data Model (FTE-months: 2) {#wbs-2.3.1-data-model-fte-months-2 .unnumbered}

The data model is a description of all the data entities and their
logical relationships managed by the system. Access to the data
typically reflects this data model as it is used to organize the data.
In this package, we inventory the data streams and products to ensure we
can capture all of the information necessary to meet our goals. We will
define all of the catalogs and forms of the image data products to
ensure efficient and correct processing. We must also ensure that our
exported metadata is sufficient to make the data useful to the
community, particularly within the Virtual Observatory. In addition to
establishing an overall abstract model, we will also identify the
technologies we will use to instantiate the model (e.g. XML, RDBMS,
etc.), store data on disc, and track information through the processing
pipelines. We expect that our model will evolve during the development
phase as we better understand the processing techniques.

#### WBS 2.3.2 Standard Collection Access: (FTE-months: 7)  {#wbs-2.3.2-standard-collection-access-fte-months-7 .unnumbered}

The goal of this package is to provide uniform access to both
proprietary data (by scientists and pipelines within the project) and
public data (by outside users). Logical access typically reflects the
data model used to organize the data. This package must incorporate the
two main types of data products we manage, namely, files (primarily
images) and catalog data (stored in databases). It must also incorporate
the different product levels and the access rights associated with each
of them. The data model will be used then to organize the data products
both in physical storage and in the logical views presented by access
interfaces. There will be a variety of access interfaces: programmatic
access locally differs from programmatic access across the grid using
the techniques of the Virtual Data Toolkit (VDT), and both are different
from public access to the data via the protocols of the Virtual
Observatory (VO). Interactive access is also important: web browser
based methods are crucial for efficient community scientist access to
the data and often scientists within the collaboration will find this
useful to download proprietary data. To the practicing scientist wget
and rsync are often the data transport mechanisms of choice. In all of
theseforms of access, access rights must be handled transparently, or at
least in simple clear ways. Access to file data will be based on logical
identifiers as this allows mapping to the closest (in terms of the
network) physical copy of the file from any one of the replicated
archives or data caches at any of the sites.

This package may also be able to leverage the emerging data management
collaboration between NOAO and NCSA; however, we should plan to handle
the development within this work package. Since data models are
ultimately project specific, there is limited opportunity for reusing an
existing design; however, we will leverage the considerable experience
among the partner institutions in this area. We will use an
off-the-shelf database that can be easily deployed at all sites; it will
be used to store the observation catalog and the various science
catalogs. We will employ a grid community tool like SRB or the replica
location services in the VDT to track replicas of files and databases
across our sites. SSL and the Globus Security Infrastructure (GSI) will
be employed to honor access rights. Interactive, browser-based access to
data also tends to be project specific (as it reflects the data model);
thus, a fair amount of implementation (albeit, based on access patterns
typical of many data archives) will be need here. This will include
catalog search and browsing pages. We plan to use existing software to
enable the cut-out service. Strategic use of VO standards (like VOTable
for exporting catalog data) will allow us to leverage existing data
browsing tools. The other component of interactive access is support for
downloading data in bulk; here we can leverage the Data Retrieval Tool
(DaRT), currently employed by users of the BIMA Data Archive. There is
some activity within the Grid community to provide tools and standards
for database access; we will consider any such tools that might exist
and are sufficiently mature.

#### WBS 2.3.3 Archive Replication: (FTE-months: 3)  {#wbs-2.3.3-archive-replication-fte-months-3 .unnumbered}

The purpose of this package is to allow transparent replication of
portions (or, in principle, all) of the DES archive. The replication
framework will be based on the organizing principles of the project data
model. This package will define an interface that compliant archive
mirrors may use to request bulk replication of file-based data via whole
collection identifiers. A separate interface will be needed for database
replication. Data replicas will be managed by a replica management
system deployed via package 4.2.2.1. The actual data copying will also
be handled by the same data transfer system as described by the
"Archiving" package.

### WBS 2.4 Processing Pipelines: (51 total FTE-months) {#wbs-2.4-processing-pipelines-51-total-fte-months .unnumbered}

#### WBS 2.4.1 Grid-based Pipeline Framework: (FTE-months: 9)  {#wbs-2.4.1-grid-based-pipeline-framework-fte-months-9 .unnumbered}

The goal of this package is to provide a common data retrieval and
execution environment for pipelines to run at any of the processing
sites in our system. This grid-base environment will feature a set of
common services that provide:

-   **Access to data through logical identifiers.** This allows any
    platform at any site to

retrieve any data in a location-independent way.

-   **Authentication and Authorization Services.** These will be used to
    honor access

rights to data and computing resources.

-   **Pipeline job execution.** The framework will provide a common
    method for executing

applications that hides platform-specific details.

-   **Workflow management and execution.** This allows a complex chain
    of pipelines to

executed and monitored.

-   **Process monitoring, error detection, and error recovery.** We will
    have an

> automated way of capturing and monitoring output messages from
> processes and detecting problems. This facility is key to enabling
> recovery from common environment-related errors (e.g. network is
> down).

-   **Automated archiving of processing products.** When a pipeline has
    finished the

> processing for a set of data, the products will be automatically
> ingested into the archive.

The existing Virtual Data Toolkit provides a convenient package that
implements these services. In addition to these services, the framework
will adopt a programming model that hides details about the data and
execution management from the software modules containing the science
algorithms. This will ultimately make the modules more portable and
applicable beyond the automated DES pipeline processing (e.g.
application to guest observer use of the DES camera).

These services will be "grid-based" in the sense that we will employ
existing, off-the-shelf tools from the grid community that are designed
for flexible, distributed computing. Among the existing grid solutions
that are relevant to our system are:

-   **Data Access:** SRB, Globus Replica Location Service (RLS), gridftp

-   **Security:** Globus Security Infrastructure (GSI), ssh

-   **Job execution and workflow management:** Globus GRAM, Condor,

Chimera/Pegasus, OGSE OGRE.

-   **Process monitoring:** Globus GRAM

> We will benefit from the considerable experience at NCSA and Fermilab
> in grid computing.

#### WBS 2.4.2 Single-Frame Calibration Pipeline: (FTE-months: 12)  {#wbs-2.4.2-single-frame-calibration-pipeline-fte-months-12 .unnumbered}

This pipeline will take raw level 0 data and reduce them to level 1
data. We expect the level 0 data to be in the form of 128 FITS files,
one for each readout amplifier in operation (2 per chip). Much of the
reduction of a single pointing will essentially be the independent
reduction of 128 4Kx1K CCD images. Reduction will include bias
subtraction, flat fielding, fringe correction, cross-talk correction (if
needed), pupil ghost correction, and masking of bad pixels. Astrometric
solutions will be recorded using WCS parameters within the image
headers. A photometric zeropoint, associated uncertainty and source will
be recorded in each header, using either the photometric solution
obtained from standard star observations that night, crude calibration
using the plate photometry of the USNO-B2 catalog, or previously
calibrated DES data taken over the same portion of the sky. These level
1 data will be the primary inputs to produce the co-added imaging, and
all level 1 and level 0 imaging data will be archived and released to
the public 1 year after acquisition. It is important that the
single-frame calibration pipeline be relatively fast; a goal will be to
produce the level 1 products from the level 0 products during the time
it takes to obtain the next set of level 0 products. While at the
primary processing station at NCSA this is not absolutely necessary
(because full nights can be processed in parallel), having a fast
pipeline will allow a more complete data quality assessment to be run on
the data flowing from the camera each night, and this will enable more
rapid feedback to the survey operator.

There are many tools available in the community that can be drawn upon
to build this pipeline. Tools of special relevance are those currently
in use in the SM/SN MOSAIC survey pipeline at CTIO and the MOSAIC
imaging pipeline under development at NOAO. Using these and other
existing pipelines, we will produce a pipeline tuned to the data from
the DECam, with emphasis on parallelization and fully automated
execution.

Issues that will require special effort will be the astrometric solution
for the field. We will obtain imaging data of open clusters and other
relatively dense stellar fields to characterize the field distortions.
This together with the relative positions of the 62 DECam devices,
should in principle allow an accurate astrometric solution over the full
field to be calculated with only a few free parameters: (a) the field
center in Right Ascension and Declination and (b) the amplitude of
atmospheric dispersion variation over the field (which will depend on
air mass, filter, and pointing direction). While in principle there
should be more than enough stars (\~10^3^) to calculate an astrometric
solution directly in each field, fully characterizing the field
distortions and then using that mapping in deriving the astrometric
solution could offer several advantages: (a) stellar positions from only
a few of the chips would be needed, reducing the computation required to
calculate the solution (especially important on the mountain), (b) using
a well characterized distortion map should lead to more accurate
astrometry assuming the chips do not wander with time, and (c) bringing
together all catalogued astrometric standards from the full field for an
astrometric solution places limits on the parallelization of the single
pointing calibration pipeline.

Lastly, we will have to consider whether production of masks are
necessary at this stage. The user, whether they be pipelines downstream
or community scientists, often want to know which pixels in the image
are saturated, which pixels are affected by bleed trails, which pixels
are on or adjacent to bad columns, which pixels are affected by cosmic
rays, and even which pixels are affected by satellites and meteor
trails.

#### WBS 2.4.3 Co-add Pipeline: (FTE-months: 12 months)  {#wbs-2.4.3-co-add-pipeline-fte-months-12-months .unnumbered}

The level 1 products in each band will be combined into images of
greater depth, uniformity and photometric accuracy. We refer to these
co-added images as the level 2 data, and we expect that the bulk of DES
science (excepting time domain science) will be carried out on
catalogues created from an analysis of these level 2 images. The
structure of the level 2 images will be FITS files containing square
tilings of the full survey region. The solid angle of these square
tilings could range from 1 deg^2^ to 10 deg^2^, and the sampling should
be approximately a factor of two finer than the intrinsic sampling of
the detectors (i.e. 0.125 arcsec pixels in the level 2 data). This
implies a library of 500 to 5000 level 2 images for each of the four
bands. Characteristic sizes of these images would range from 3GB to
30GB, depending on the solid angle and the adopted pixel size.

The process of combining multiple images of the same part of the sky is
complex, because in general the image quality and depths vary from
exposure to exposure. Nearby objects can even move from epoch to epoch,
complicating the co-adding procedure. There are several existing
techniques and software modules used to combine multiple images of the
same part of the sky. In general, the surface brightness within a single
pixel in a level 2 image will be estimated from pixels or sets of pixels
within multiple level 1 images by a mapping that conserves flux. The
co-adding process requires combining these multiple constraints in a
manner that accounts for variations in seeing, zeropoint and image
noise, and thus we will have to track both the number of images that
went into each pixel and the error associated with each pixel, for every
pixel within a level 2 image. We will test some of these schemes with
emphasis on the returned photometric accuracy and combined image
quality. Ultimately, we will likely deploy a tool based on several
packaged that will produce the images that make up the map of the level
2 on the available level 1 products for a variety of critera: best
seeing, deepest photometry, smoothest PSF. These different filterings of
the level 1 products are driven by different science goals; ; for
example, the weak lensing science may benefit from a more stringent
seeing limit that will sacrifice depth, whereas the galaxy and galaxy
cluster studies will benefit primary from depth as long as star-galaxy
classification isn't compromised.

Level 2 processing will generally be carried out multiple times, and it
is this processing which will benefit most from the grid-based computing
model. In principle, each level 2 image is independent of the others
(although in the end we will want to stitch together the photometric
zeropoints to yield seamless transitions from one image to the next and
to maintain photometric uniformity across the full survey). Thus, the
level 2 processing is easily parallelized, and so tasks can be carried
out in parallel on hundreds to even tens of thousands of processing
nodes.

In addition to the actual co-add pipeline, we will require tools that
can extract images of a particular size, sampling, and location from
this archive. There are several image extraction tools available, and we
will adopt one of those for this purpose.

#### WBS 2.4.4 Source Extraction Pipeline: (FTE-months: 12)  {#wbs-2.4.4-source-extraction-pipeline-fte-months-12 .unnumbered}

Cataloging will be carried out for each image in the creation of the
level 1 data and in quality assessment. In addition, the science
cataloging will be carried out primarily on the level 2 data. For this
discussion, we consider the source extraction pipeline to be the
cataloging of objects detected in the level 2 data. These catalogs will
be considered the level 3 data, and they will be released to the
community with the level 2 data.

There are many available cataloging programs. A current favorite in the
astronomy community is SExtractor, a program written by Emanuel Bertin.
We will adopt SExtractor as our standard cataloging program, but it is
likely that special purpose software will be required to support our
science goals. For example, weak lensing analysis requires the
characterization of galaxy and stellar shapes, and so we will likely
require more than the functionality offered by SExtractor. Furthermore,
SExtractor does not handle varying seeing in a natural way: we will need
to measure PSF magnitudes for the stars for a variety of purposes,
including photometric calibration. It will likely be important to have
not only galaxy colors calculated from each band independently, but also
colors over the same physical portion of each galaxy; the latter
requires adopting the galaxy region definition from one band in the
photometric calculations in all the bands, and this will likely require
functionality beyond that offered in SExtractor. In general, we could
simply run SExtractor to characterize objects, their positions and their
photometric properties, and then use these initial catalogs to guide
additional image calculations by other tools.

We note that the SDSS Photometric Pipeline could in principal be used.
Given our experience with using it on non-SDSS data, we are much more
likely to use the toolkit photo, which the Photometric Pipeline is built
out of, to take advantage of some of the sophisticated algorithms
developed and tested by the SDSS. Since it is modular and easily
extendable, we can without much development use the tools on the
positions found by SExtractor.

### WBS 2.5 Science Analysis Pipelines: (3 total FTE-months) {#wbs-2.5-science-analysis-pipelines-3-total-fte-months .unnumbered}

As is described below, much of the work associated with the science
analysis will not be considered a contribution to the data management
part of the project, but rather will be charged against the science time
of the participating astronomers. Nevertheless, for the purposes of
tracking integration with the rest of data management, we list these
activities below.

#### WBS 2.5.1 Photometric Redshift Pipeline: (FTE-months: 3 months)  {#wbs-2.5.1-photometric-redshift-pipeline-fte-months-3-months .unnumbered}

This pipeline will take the photometry in four colors for all extended
objects and estimate photometric redshifts and associated uncertainties.
Huan Lin has existing tools, but they will need to be folded into the
grid based processing framework. This is central to essentially all the
science, and so folding this pipeline into the project data framework
should be considered a part of the data management effort.

#### WBS 2.5.2 Cluster-finding Pipeline: (FTE-months: 0)  {#wbs-2.5.2-cluster-finding-pipeline-fte-months-0 .unnumbered}

The goal of this pipeline is to create a multi-frequency catalog of
galaxy clusters. Cluster finding in the optical and multiwavelength
cluster finding pipelines will be written as part of the science
analysis of the survey. This effort lies outside the DM domain, but
pipelines should be structured to operate within a common framework, and
data products will be available to the collaboration.

#### WBS 2.5.3 Weak Lensing Analysis Pipeline: (FTE-months: 0)  {#wbs-2.5.3-weak-lensing-analysis-pipeline-fte-months-0 .unnumbered}

The goal of this pipeline is in part the creation of cosmic shear maps.
Weak lensing pipelines will be written as part of the science analysis
of the survey. It is also likely that additional, co-added datasets to
optimize PSF quality will be produced and archived. This effort also
lies outside the DM domain, but pipelines should be structured within a
common framework, and data products will be available to the
collaboration.

#### WBS 2.5.4 Synoptic Analysis Pipeline: (FTE-months: 0) {#wbs-2.5.4-synoptic-analysis-pipeline-fte-months-0 .unnumbered}

General synoptic analysis pipelines will be written as part of the
science analysis of the survey. These pipelines will include SN
detection and light curve measurement tools similar to those already
being used to analyze the SM/SN data from the CTIO 4m. A quick alert
system will be put in place to facilitate spectroscopic and photometric
followup by the larger community. In addition, the main survey data will
enable a study of variable objects over longer timescales with much
cruder sampling. These existing pipelines will have to be brought within
the common framework, and data products should be available to the
collaboration.

### WBS 2.6 Data Challenge (7 total FTE-months) {#wbs-2.6-data-challenge-7-total-fte-months .unnumbered}

A critical part of the preparation for first light will be the
verification of our processing system to ensure the scientific integrity
of our data products. Verification will be accomplished through a series
of "data challenges" in which we create simulated data based on a model
of the universe, pass it through our processing system, and attempt to
recover the model through analysis of the resulting products. In
addition to scientific integrity, we will also test the technical
robustness of the system, ensuring it can handle the required data rates
and expected modes of failure.

Significant portions of this work will be centered at Fermilab,
including the creation of simulated data and analysis of results;
consequently, this particular part of the work will be tracked through a
separate WBS (1.3). The portions of the work tracked under the Data
Management part of the project cover the integration of the simulator
with the processing system and execution of the actual challenges; these
are enumerated below.

#### WBS 2.6.1 Simulation Interface and Integration: (FTE-months: 3)  {#wbs-2.6.1-simulation-interface-and-integration-fte-months-3 .unnumbered}

This package defines the interface that allows simulated data to be
injected into the processing system. This may in fact be designed as
several interfaces that allow data to be inserted at several places in
the chain. We allow most of the time in this package to be focused on
general integration issues and testing prior to use by an actual data
challenge.

#### WBS 2.6.2 Data Challenge I: (2 FTE-month) {#wbs-2.6.2-data-challenge-i-2-fte-month .unnumbered}

In this challenge, our simulated data will be adapted from actual data
from similar scientific instruments (e.g. the NOAO Mosaic Camera). The
purpose is to assess existing software and conduct preliminary tests of
techniques we expect to use in our processing. The processing will be
done largely "by hand," by which we run software interactively. Our goal
is to capture all of the processing chain from calibration to object
catalog generation (which, given existing software in the community,
should be easily accomplished). We will use the results to refine our
design and further discern the development and integration costs.

#### WBS 6.2.3 Data Challenge II: (1 FTE-month) {#wbs-6.2.3-data-challenge-ii-1-fte-month .unnumbered}

The purpose of this challenge is to test the archive and pipeline
framework. The input data will likely be a combination of simulated data
and real data adapted for this challenge. We will ingest the data into
the archive as if it is part of the raw data stream and automatically
initiate single-frame calibration.

####  {#section-3 .unnumbered}

####  {#section-4 .unnumbered}

#### WBS 2.6.4 Data Challenge III: (1 FTE-month) {#wbs-2.6.4-data-challenge-iii-1-fte-month .unnumbered}

The purpose of this challenge is to provide a full stress test of the
entire data management system from data ingest to co-addition. The input
will be a full year's worth of simulated raw data (provided via WBS
1.3.4; see Chapter 7). The challenge will include simulations of
selected failure modes (e.g. network failures, machines going down
during processing). When the processing is complete, the data challenge
analysis team (WBS 1.3.5) will analyze the data to verify sufficient
recovery of the input imaging model.

### WBS 2.7 Survey Data Processing Operations (216 total FTE-months) {#wbs-2.7-survey-data-processing-operations-216-total-fte-months .unnumbered}

After the build phase of the project, the survey will begin and the data
processing engine will begin operations. The survey data acquisition
lasts 5 years; we adopt a 6 year timescale for the survey data
processing. Survey data processing operations will involve maintenance
of the processing platforms and pipelines at CTIO, in La Serena, at NCSA
and at Fermilab. In addition, quality assessment of the data will be an
ongoing, hands-on process that we expect will be driven by the science
progress in the dark energy studies. A person will have to be available
to respond to inquiries from the public regarding the publicly released
data. Feedback from the data quality assessment will naturally provide
input for pipeline improvements. We estimate that this task will be
require approximately 3 FTE, and this effort will be shared between U
Illinois and Fermilab. Over the six years of the survey data processing,
this corresponds to 18 FTE years. Funding for this portion of the
project will be requested as the DES build phase nears an end.

### WBS 2.8 Dark Energy Science Analysis {#wbs-2.8-dark-energy-science-analysis .unnumbered}

During the survey operations phase the science effort will begin. The
science analysis will be a critical data quality feedback tool, and the
science results are the underlying motivation for this project. We will
request funding for this portion of the project as the DES build phase
nears an end.

**7. Project Management Plan**

We will manage the DES as two projects, the Survey Instrument and the
Data Management, during the construction and commissioning phases of the
Survey. Each project has separate well-defined deliverables that will
mark the end of the construction and commissioning phases. The primary
Survey Instrument project deliverable is a working instrument that has
been fully integrated and commissioned on the Blanco telescope. The Data
Management project has two deliverables software and hardware systems;
one that can process the data from the Survey Instrument and the second
that can archive and then distribute the processed data to the DES
collaboration and after a period of quality validation, distribute the
DES Archive to the astronomical community. The delivery of mock data and
survey quality commissioning data by the Survey Instrument team to the
Data Management team and the subsequent processing of the mock data and
the commissioning data by the Data Management team are the important
deliverables that tie the two projects together. The processing of
survey quality commissioning data and its subsequent delivery to the
archive marks the end of the commissioning phase of the Survey and the
start of Survey Operations.

We anticipate that once observations begin the Survey Instrument team
will be responsible for the maintenance of the Survey Instrument and the
Data Management Team will be responsible for production data processing
and distribution of the data and catalogs, including data quality
assurance of the data products. Both teams in partnership with CTIO
operations will contribute to survey operations on the mountain required
to acquire the survey data. The primary and most important Survey
deliverable is the Dark Energy Survey Archive since it will enable both
collaboration and community to obtain scientific results from the data.
While we discuss some of the strategic elements of a survey operations
management plan in this chapter we are still developing this part of the
Project Management Plan.

Each project has its own project management team, Work Breakdown
Structure, budget, schedule, and project management plan, including a
change control process. This choice recognizes that the nature of the
work in each project is very different and that schedules for the two
projects become strongly coupled only near the end of the construction
phase. Thus the two projects will proceed more efficiently if each is
delegated adequate authority and if the project teams are allowed to
manage their work somewhat independently. Because an appropriate level
of active management of the two projects is needed, we have created a
Management Committee to coordinate and oversee the efforts of the two
projects throughout the life of the two projects. Moreover, we
anticipate that the Management Committee will continue to coordinate the
work of the Survey Instrument Team and the Data Management Team during
the five-year operations period. The Management Committee will also be
responsible for obtaining the financial and personnel resources that are
needed to carry out the two projects.

The remainder of this chapter describes the management structure
outlined above in more detail starting with the Management Committee.

**7.1 Management Committee**

**7.1.1 Management Committee Responsibilities and Authority**

The Management Committee (MC) is responsible for broadly defining the
hardware and software interfaces between the two projects. The Project
Managers will develop an implementation plan for these interfaces that
is consistent with the broad definitions and the MC will approve the
plan. It is also responsible for preparing agreements between the Dark
Energy Survey and the participating institutions, CTIO operations and
NOAO Data Products Division. The current list of the responsibilities of
the Management committee is as follows:

1.  Develop and approve fund raising strategies.

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1.  Organize preparation of proposals for funding.

2.  Organize presentations to the funding agencies (DOE, NSF & private)
    and federal agencies that set funding policy (OMB, OSTP).

3.  Coordination of the work and the schedules of the two projects and
    their interfaces with CTIO and the NOAO Data Products Division.

4.  Preparation of documents for NOAO and AURA committees that oversee
    Blanco operations.

5.  Preparation and approval for the DES agreements between the
    participating institutions and the DES.

6.  Preparation of an annual budget for the Survey sponsors.

7.  Serve as the highest-level change control board for the Survey. This
    will include any necessary changes to the management of the
    projects, the primary science requirements, significant changes in
    cost and schedule.

8.  Schedule collaboration meetings and approve their agendas. The host
    institution is responsible for the meeting.

    2.  **Management Committee Membership**

MC has at least one representative from each of the participating
institutions. The current membership of the MC is as follows:

> Chair John Peoples (alternate B. Flaugher)
>
> Instrument Project Leader Brenna Flaugher
>
> Data Management Leader Joe Mohr
>
> Fermilab rep Jim Annis (alternate B. Flaugher)
>
> Chicago rep Josh Frieman
>
> CTIO/NOAO rep Alistair Walker (alternate T. Abbott)
>
> U of I rep Jon Thaler (alternate J. Mohr)
>
> LBNL rep M. Levi (alternate S. Perlmutter)

**7.1.3 The Change Control Process**

A formal change control process will be used to coordinate and oversee
the work of the two projects. The Science and Technical requirements,
presented in Chapter 3, flow from the Survey Science Program, described
in Chapter 2. The Science and Technical requirements, the total baseline
cost of each project, and the major project milestones constitute the
level 1 requirements. The Management Committee is responsible for
defining the level 1 requirements and for assuring the DES sponsors that
the level 1 requirements will be met. The level 2 requirements will flow
from the level 1 requirements and will be defined by the technical
specification of level 2 elements in the WBS, the schedule for the level
2 elements and their cost. While the project managers are responsible
for meeting the level 2 requirements, changes to level 2 requirements
will be proposed by the project managers and approved by the Management
Committee. Prior to baselining the Survey the level 2 and level 3
technical specifications, costs, budgets, and schedule milestones for
each project will be proposed by the respective Project Managers and
then approved by the Management Committee. We anticipate that the Survey
Baseline, which will consist of the level 1 requirements, will be
established by the sponsoring agencies following the Baseline Review.

Each project manager will have the authority to make changes to the
level 3 technical specifications, cost, and schedule provided that they
do not change the level 1 or level 2 requirements. The Project managers
will be responsible for managing changes to the level 3 requirements,
which are defined by the technical specifications, costs, and schedule
at level 3 in the WBS. The change control board for each project will
process level 3 changes and decisions and the background for the
decisions will be reported to the Management Committee in writing for
information. Each project manager will be allocated a fraction of the
annual allocation of contingency funds to manage problems appropriate to
level 3.

**7.1.4 Science Working Group**

The Science Working Group developed the science program contained in
Chapter 2. It continues to be responsible for the further development of
the scientific program and the refinement of the DES science goals.
While the Science team is open to all collaboration members, Frieman,
Mohr and Annis have led the development of the proposal science with
significant contributions from Aldering, Hu, Lin, Kent, Perlmutter,
Sheldon, Smith, Suntzeff, Thaler, and Wechsler. The Science Working
Group reports to the Management Committee. When it is time to prepare a
new proposal or revise an old proposal for funding, the Management
committee will ask the Science Working Group to review the science
material, particularly the relationship of the DES to other projects.

**7.1.5 Project Advisory Board**

Prior to baselining the projects, the DES will recruit a committee of
technical and management experts to review each project. The Project
Advisory Board (PAB) will advise the DES Management Committee on the
status of the project. It is intended that the committee will meet at
least once a year to review the project status and provide advice to the
Management Committee until the commissioning phase is complete. The PAB
will submit a report describing its findings and advice to the URA/DES
oversight board.

**7.1.6 Collaboration Policies**

The Collaboration will be organized so that the key science projects,
described in chapter 2, can be executed and published by the
participants. At the appropriate time the Management Committee will
authorize the creation of a Collaboration Council that will define
policies for data access and publications and that will guide the
science effort of the Collaboration. The policies will be subject to
approval by the Management Committee.

**7.2 Survey Instrument (WBS 1.0)**

Fermilab is the lead institution with contributions from all the
participating institutions. Brenna Flaugher (Fermilab) is the Survey
Instrument Project Manager and Tim Abbott (CTIO) is the Deputy Project
Manger. Jim Annis serves as the Instrument Project Scientist.

**7.2.1 Survey Instrument project Work Breakdown Structure**

The Dark Energy Survey Instrument project is divided into seven level 2
projects as shown in Figure 7.1.

![](media/image42.png){width="5.997916666666667in"
height="6.573611111111111in"}

Figure 7.1 Survey Instrument project WBS elements.

Management, WBS 1.1, captures all the costs associated with the Survey
Instrument construction project management. It covers preparation of
schedule and costs, management and specification of technical
requirements, active tracking of progress of the project through
periodic reports and reviews.

As shown in the Figure 7.1, the Instrument, WBS 1.2, and the Survey
Planning, WBS 1.3, elements are further divided into Level 3 elements.
These were described in detail in chapters 5 and 4 respectively. WBS
elements 1.4, telescope improvements and 1.5, commissioning at CTIO, are
both activities that will occur at CTIO and Tim Abbott is leading those
efforts. System Integration is led by Peter Limon (FNAL). He is
responsible for defining a system for assuring that all the components
of the instrument project can be successfully integrated. WBS 1.7
supports the scientific effort of the Survey Instrument team and
miscellaneous costs that it needs to carry out the science analysis of
the archived data and to prepare scientific papers.

**7.2.2 Survey Instrument Project Organization**

A description the Survey Instrument construction and commissioning tasks
by WBS element at (Level 3) is presented in this section and will form
the basis for the Survey Instrument project management plan. The level 3
leaders will work with the Project Manager to develop cost and schedule
and provide monthly updates, in addition, to the specific tasks listed
below.

1.2.1 CCDs covers the procurement of the CCDs, including the preparation
of a QA plan for wafer fabrication and processing, managing the
procurement, as well as the thinning and dicing. Natalie Roe (LBNL) is
the L3 task manager.

1.2.2 CCD packaging covers the design and implementation of the CCD
packaging in conjunction with the Front End electronics group,
development of a QA system, and oversight of the packaging assembly
process. Greg Derylo (FNAL) is the L3 task manger.

3.  Front End Electronics covers the design and implementation of the
    electrical system from the CCDs to the DAQ system (WBS 1.2.5)
    working with the CCD packaging group (WBS 1.2.2) on the AlN board
    and with the UIUC group on the interface to the DAQ. This also
    includes development of appropriate QA measures for the various
    components and oversight of the testing and construction of the FE
    system. William Wester and Terri Shaw are the L3 task managers.

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3.  CCD testing and grading covers development of a testing plan,
    designing and developing adequate testing facilities, developing
    testing documentation (e.g. paper travelers and a web based tracking
    system), over site of the testing and grading efforts, developing a
    plan for testing the CCDs after they are installed in the focal
    plane, and developing a testing plan for the fully assembled focal
    plane. William Wester is the L3 task manager.

4.  Data Acquisition (DAQ) covers the design and implementation of the
    electrical system starting from the connection to the front-end
    electronics to the output of an assembled image on disk. This also
    includes providing advice and support for the DAQ at the CCD testing
    facility at Fermilab, working with the Front End group to define the
    connections between the DAQ and the front end electronics, working
    with the data management group to define the output format of the
    images and the transmittal of data into the data management system.
    Jon Thaler is the L3 task manager.

5.  Camera Vessel and Focal plane covers design of the camera vessel and
    focal plane in conjunction with the cooling, packaging and front end
    electronics groups, developing an assembly plane for the camera
    including installation of CCDs into the focal plane, cable routing,
    connections from cooling system to the focal plane, working with the
    FE, DAQ and cooling groups to develop and implement a testing plan
    for the fully populated focal plane. Herman Cease is the L3 task
    manager.

1.2.7 Cooling covers the design and implementation of a system capable
of setting and maintaining the CCDs at a constant and well-known temp,
developing a cooling system for any additional heat sources in the
primary cage, documenting and developing thermal management plans for
everything in the cage, developing a testing plan for the cooling system
on the full focal plane, and providing support for cooling systems in
the CCD test stations. Del Allspach is the L3 task manger.

1.2.8 Corrector includes optical design, contracting and working with a
professional optical engineer, procurement and testing of the optical
components, and developing an assembly and testing plan for the
completed corrector. Steve Kent and French Leger are the L3 task
mangers.

1.2.9 Prime focus cage covers the design and construction of the primary
cage and all of the systems external to the camera and corrector. This
also includes handling of the F/8 secondary, alignment to the primary,
the shutter and the connection between the camera and the corrector, and
providing support if needed for an in situ full system calibration
system. French Leger is the L3 task manger.

1.2.10 Assembly and testing covers the final assembly of the completed
and fully tested main systems (e.g. camera, corrector, prime focus
cage). This will evolve from the testing and QA procedures of the
individual components.

**7.2.3 Manpower and Coordination Activities**

This section outlines the contributions of the DES collaboration
institutions to the DES Instrument project. Both Fermilab and UIUC will
contribute technical labor to the DES instrument project and these
contributions will be outlined in the next section.

**7.2.3.1 Fermilab**

Fermilab is the lead institution on the DES Instrument project. The
Project Manager, Brenna Flaugher, coordinates the efforts of the
Fermilab staff on the Instrument project and directs the lead scientists
and engineers who are responsible for the Level 2 and 3 projects.
Typically one videoconference meeting per week is devoted to the
Instrument design and nearly all of the people working on the Instrument
participate.

Within the Fermilab Particle Physics Division (PPD), William Wester
coordinates the Electrical Engineering Department participation in the
area of the front-end electronics and the CCD testing efforts and Brenna
Flaugher coordinates the mechanical Engineering Department support in
the areas of CCD packaging, the camera vessel, the cooling system and
the prime focus cage. To supplement our experience with silicon
detectors, we are exploring a partnership with Richard Stover at Lick
Observatory since he has experience packaging the LBNL CCDs, with the
goal of establishing a technology transfer program during the
development phases of the CCD work (Phases A, B, and C).

Jim Annis and Huan Lin, members of the Computing Division Experimental
Astrophysics Group, lead the survey planning effort and will concentrate
on designing a survey which can achieve our science goals with
particular focus on the photometric calibration. This task will expand
to include the simulations that are relevant to observing strategy and
the mock data challenge. The planning for the mock data challenge will
begin in September 2004, and it will be done jointly with the Data
Management team. The Survey Strategy team will include liaisons from the
Data Management team, the Science team, and the Dark Energy Instrument
team, who will serve as the points of contact.

**7.2.3.2 LBNL**

Natalie Roe is the L3 project leader for the CCD procurement project and
Mike Levi is the LBNL representative on the DES management committee.
They, and other members of the LBNL groups, have given the Instrument
Team considerable guidance on how to plan the acquisition of the CCDs.
LBNL will manage the procurement of the CCDs from the wafer vendor,
their subsequent thinning, processing and dicing of the devices. LBNL
will test the devices on a cold probe station and deliver them to FNAL
with the testing results.

**7.2.3.3 CTIO**

Tim Abbott is the project deputy project manager and the primary contact
with CTIO. He is also the L3 project leader for the activities that will
occur at CTIO such as telescope improvements and commissioning of the
instrument. He provides critical information on the Blanco performance
and the Cerro Tololo infrastructure. The DAQ working group (WBS 1.2.3
and WBS1.2.5) is also working with the developers of the Monsoon system
at CTIO and Tucson to understand and adapt the current system to our
needs. We are investigating additional partnership arrangements with
CTIO including assistance with the fabrication of the prime focus cage,
development of a Sol-Gel coating facility that could handle the large
optical elements of our corrector and development of the infrastructure
to accommodate the large cooling facility needed for our instrument
among others.

**7.2.3.4 University of Chicago**

University of Chicago will take the lead in the design, acquisition and
testing of the optics. Steve Kent (Chicago and Fermilab) will lead the
effort with contributions from French Leger (FNAL) and Mike Gladders
(Carnegie Obs.). They have significant experience in the design,
construction and procurement of optical systems. An optical designer
will supplement their efforts when funds become available. The optical
designer will be charged with exploring cost savings, the preparation of
the procurement documents and the specifications for acceptance testing
at the vendors, and the design of an optics test facility at Fermilab
suitable for testing the individual lenses and the complete corrector.
We anticipate extending the contract to cover oversight of the work at
the vendors.

**7.2.3.5 UIUC**

John Thaler of U Illinois leads the design of the Data Acquisition
System and its integration with the front-end electronics and CTIO
infrastructure. He will be responsible for ensuring the system meets the
scientific requirements. Todd Moore, the DAQ project manager, will lead
the day-to-day operations of the DAQ design and implementation. A Data
Acquisition working group will assist these two. The working group will
consist of those members of the collaboration who are working on the
various parts of the DAQ as well as others from the teams working on
front end electronics, telescope monitor and control system, and data
processing that have interfaces with the DAQ.

**7.2.4 Survey Instrument Project Cost Estimate**

The cost estimate for the Dark Energy Survey Instrument is given in
Table 7.1 in then year \$ without institutional overhead. The costs are
rolled up to level 2 except for the Instrument construction where the
details are provided at Level 3. We have followed the Fermilab/DOE
practice for preparing cost estimates by including only the cost of
technical labor (technicians, engineers and computing professionals). We
have not included the cost of salaries of senior scientists and faculty
in view of their commitments to service work on other projects and
teaching respectively. Fermilab plans to provide technical labor with a
value of \$ 6,503 k and UIUC plans to contribute technical labor with a
value of \$ 450 k to the DAQ system. The labor column in Table 7.1 is
the sum of the Fermilab and UIUC technical labor.

The Fermilab labor costs were estimated using the average salary
information in FY04 dollars (SWF) without overhead for mid range
salaries in each category and based on the number of hours estimated for
each task. The UIUC technical labor costs are similarly included in the
total for the DAQ labor. The labor costs are roughly equal to the M&S
costs with for two notable exceptions, the optics and the CCDs. Both are
very large procurements with little labor needed from the Fermilab
technical staff. The optics will be purchased as blanks from a glass
vendor (\$665k) and will then be ground to the correct shape by another
vendor (\~\$1,000k). In addition, the filters and the coatings will be
purchased from outside companies. We also will contract with an optical
engineer for the design of the corrector and to oversee the procurement
and testing of the corrector elements. This is costed as M&S because it
will be a contract for services with a non Fermilab engineer. The CCD
M&S cost was based on the Reference Design that includes processing and
testing the wafers after they are received from the wafer vendor.

**Table 7.1 Cost Estimate for the Dark Energy Survey Instrument in
then-year \$**

and excluding overhead

  --------- --------------------------------- ------------ ----------- ------------
  ** **     ** **                             **M&S        **Labor     **Total
                                              (\$K)**      \$K)**      (\$K)**

  **1**     **Dark Energy Survey Instrument** **8,368**    **4,635**   **13,003**

  **1.1**   **Management**                    **235**      **189**     **423**

  **1.2**   **Dark Energy Instrument          **7,734**    **3,692**   **11,426**
            Construction**                                             

  1.2.1     CCDs                              **2,155**    **25**      **2,180**

  1.2.2     CCD Packaging                     **287**      **607**     **895**

  1.2.3     Front End Electronics             **595**      **640**     **1,235**

  1.2.4     CCD Testing                       **579**      **364**     **942**

  1.2.5     Data Acquisition                  **167**      **299**     **466**

  1.2.6     Focal Plane, Camera Vessel        **163**      **366**     **529**

  1.2.7     Cooling                           **427**      **160**     **587**

  1.2.8     Optical Corrector                 **2,463**    **121**     **2,584**

  1.2.9     Prime Focus Cage                  **618**      **779**     **1,397**

  1.2.10    Auxiliary Components              **146**      **164**     **309**

  1.2.11    Final Assembly and Testing        **135**      **167**     **302**

  **1.3**   **Survey Strategy**               **399**      **539**     **938**

  **1.4**   **Telescope Improvements\***      **0**        **0**       **0**

  **1.5**   **Commissioning (at CTIO)**       **0**        **134**     **134**

  **1.6**   **System Integration**            **0**        **82**      **82**

  **1.7**   **Dark Energy Science+**          **0**        **0**       **0**

                                                                        

            **Contingency**                   **3,080**    **2,318**   **5,397**

                                                                        

  ** **     **Total cost (w/o overhead)**     **11,447**   **6,953**   **18,400**
  --------- --------------------------------- ------------ ----------- ------------

\* CTIO will support the costs of the telescope improvements that were
anticipated in the AO. The DES collaboration will support the cost of
other upgrades only if they significantly benefit the DES project. At
this time, none are planned but we are discussing possibilities for
reducing the slew time from 35 sec to \~ 17 sec, the anticipated time to
read out the CCDs.

\+ Dark Energy Science will be carried out by the scientific staff
university faculty. It will treated as a contribution from the
scientists' and/or faculty members' institution. This WBS element will
capture the additional costs for scientific investigations.

In estimating total project costs for the DOE, it is standard to
separate the total project cost into a base cost estimate plus a
contingency. The base cost estimate covers the current best estimate of
the cost of the project. The total contingency is derived by separately
assigning a contingency factor on each task based on the confidence in
the base cost estimate. Typically at such an early stage of a project a
contingency of \~40-50% is expected. History has shown that, this
contingency money has always been needed to complete the project. As a
result DOE, considers the total project cost as the sum of the base
estimate and the contingency based on the expectation that this
represents the best and realistic estimate of total cost.

We have followed the Fermilab/DOE practice in estimating and assigning
the contingency for the DES instrument as follows. A contingency factor
was assigned at the task level. A general factor of 40% was taken for
everything unless a quote from a vendor had been obtained. In these
cases (corrector blanks, DAQ equipment) 10% was taken. For the CCDs, the
contingency has two components. First is a 20% contingency on the
detailed estimate from LBNL on the costs for procuring the CCD wafers
and processing them at the LBNL Microsystems Laboratory. In addition,
the cost of an extra 24 wafer run has been included, bringing the total
contingency on the CCDs to 43%. The resulting contingency on the M&S
cost is 36% for a total M&S cost of \$11,447 K. For contingency on the
labor costs we assume 50% at this time for all tasks. This results in a
total labor cost of \$6,953 K and a total project cost of \$18,400 K in
then-year \$, exclusive of institutional overhead. When that overhead is
included the total project cost of the Survey Instrument (WBS 1.0) is
\$22.5M.

We show UIUC DAQ personnel in Table 7.2. The people listed are all
members of the UIUC high energy physics group. There are sufficient
personnel to complete the telescope DAQ on schedule. The costs are
included to show the contribution of the UIUC group supported by the
UIUC HEPG grant from DOE to the DES instrument project. Only the
technical labor costs in this table are included in the total in Table
7.1.

**Table 7.2.** UIUC DAQ Personnel

  ------------ --------------------------- ---------------- --------------
  **Name**     **Role**                    **Effort\        **Cost\
                                           **(6/2004 →      **(with
                                           6/2009)          overhead)

  Jon Thaler   Physicist, DAQ project      50%              \$125 k
               scientist                                    

  Todd Moore   Engineer, DAQ project       75% → 100%       \$623 k
               manager                                      

  Inga         Physicist                   50% → 80%        \$538 k
  Karliner                                                  

  Mats Selen   Physicist                   20% → 50%        \$79 k

  Allison      Technician                  As needed        \$105 k
  Sibert                                                    

  TBD          Physicist (post-doc)        0% → 100%        \$360 k

  TBD          Physicists (1-2 graduate    0% → 100%        \$271 k
               students)                                    

  **Total                                                   **\$2101 k**
  cost**                                                    
  ------------ --------------------------- ---------------- --------------

Cost includes summer salary (at level of effort) for faculty, and fully
loaded salaries for personnel supported by the UIUC HEPG grant from DOE.

**7.2.5 Funding Model for Survey Instrument M&S costs**

The estimated cost of the materials and services for the Survey
Instrument project, after including a contingency of 36% and excluding
overhead, is \$11.4 million. We propose that DOE provide \~65% of the
required M&S funds and that we obtain other funds for the remaining
\~35% of the M&S cost. Table 7.3 presents the funding profile that is
required to meet the proposed schedule that allows commissioning to be
completed in May 2009. We believe that we could complete commissioning
by October 2008 if the funding profile did not constrain the placement
of orders. The placement of some of the corrector optics cannot be made
until the beginning of FY07 thereby introducing a delay of 7 months
relative to the schedule that is not constrained by the funding profile.
However, it reduces the peak request for M&S funds from Fermilab to
\$2.5 million (excluding overhead) in FY06 relative to the funding
unconstrained profile which required \$4.5M for M&S funds (excluding
overhead) from Fermilab in FY05. We also shift the start of CCD
production by 1 month into FY06 in order to flatten the funding profile.
This funding profile put the optics procurement on the critical path.

Table 7.3. M&S Costs in then-year \$K without overhead

  ----------------- -------- --------- --------- --------- ------- ---------
                    FY05     FY06      FY07      FY08      FY09    Total

  M&S base          1,097    3,321     3,362     520       68      8,368

  M&S Contingency   0        379       338       1580      782     3080

  Total M&S         1097     3321      3362      520       68      11448

                                                                   

  DOE - Fermilab    500      2500      2500      1100      800     7400

  Other Funding     600      1200      1200      1000      50      4050

                                                                   

  Total Funding     1100     3700      3700      2100      850     11450

  DOE Fraction      0.45     0.68      0.68      0.52      0.94    0.65
  ----------------- -------- --------- --------- --------- ------- ---------

The M&S Contingency is distributed to match the difference between the
funding and the M&S base needs.

The UIUC HEP group has provided the funds to purchase the DAQ hardware
for the first CCD test station (\~\$30K) from its FY04 DOE and those
costs are included in the FY05 costs in Table 7.3. The other funds for
the survey instrument in FY05 will be used to initiate the CCD
preproduction order and to initiate the final optical design.

We propose that Fermilab account for its overhead costs on all M&S
purchases made through Fermilab as an in-kind contribution from the
Fermilab base budget.

**7.2.5 Survey Instrument Schedule**

The schedule for the Survey Instrument is funding limited. It begins in
2004 with the design work already underway and continues through the
instrument commissioning on the Blanco. The critical path milestones are
shown in Table 7.4. The procurement of the largest optic is the critical
path item. The down selection of the CCD source is very close to the
critical path.

**Table 7.4 Critical Path Milestones for Instrument Construction.**

![](media/image43.wmf){width="6.36875in" height="2.466666666666667in"}

**7.2.6 Survey Instrument Reviews**

We anticipate having a series of meetings and reviews throughout the
course of the project. These will be motivated by the desire to
determine if significant milestones have been met and to authorize major
procurements. Each Level 3 task in the Instrument project will undergo
the following reviews

1\) Conceptual Design (ready to proceed with development and
prototyping)

> 2\) Preliminary Design (ready to proceed to preproduction)
>
> 3\) Production Readiness
>
> 4\) Annual Production updates

These will occur as appropriate to the status of the task, but will
roughly correspond to annual review, beginning in the fall of 2004.

We anticipate a full baseline review in the spring of 2005 by DOE and
Fermilab (and perhaps other sponsors). If we successfully pass the
review, we expect to receive authorization to proceed with spending
money on the optics. After this point in time changes in the design at
level 2 and level 1 will require the sponsors approval. The Project
Advisory Board will participate in this process by holding a
pre-baseline review.

We will review the Phase B test results in August 05 prior to
authorizing the production order and the processing of the rest of the
Phase B wafers.

**7.3 Data Management Project Plan (WBS 2.0)**

In Chapter 6, Data Management, we describe the technical approach of the
data management plan. This includes technical summaries of the various
work packages that make up WBS 2.0. In this section, we discuss our plan
for managing resources to deliver the data management system and process
the data over the course of the survey.

#### 7.3.1 Data Management Project Organization {#data-management-project-organization .unnumbered}

The University of Illinois Astronomy Department will lead the data
management and archiving effort. Their efforts will be supported by the
National Center for Supercomputing Applications (NCSA) and the Fermilab
Experimental Astrophysics Group. This will allow the Collaboration to
take advantage of the experience that NCSA and Fermilab have gained in
processing, archiving and distributing astronomical data. The Management
Committee has delegated the definition of the computing framework,
within which the Collaboration will develop software, including the
simulations, to the Data Management Steering Group (DMSG). The DMSG is
described in 7.3.3. The University of Chicago group also plans to
contribute to the software systems through a major contribution to the
simulations. Already several Chicago students and post docs are
contributing to the simulations. Fermilab scientists and University of
Illinois faculty and students are also making similar contributions.

Joe Mohr at U Illinois leads the Data Management project with C. Smith
(NOAO/CTIO) as a co-leader and R. Plante (U Illinois/NCSA) as the Data
Management Project Manager. The University of Illinois at
Urban-Champaign is the lead institution, with key involvement of the
Department of Astronomy, Department of Physics and NCSA. Fermilab, the
NOAO Data Products Division and the University of Chicago will make
supporting contributions. The technical plan for this project is
presented in Chapter 6.

###### 7.3.2 Data Management project Work Breakdown Structure {#data-management-project-work-breakdown-structure .unnumbered}

The Dark Energy Survey Data Management project is broken down into eight
level 2 projects as shown in Figure 7.2. The University of Illinois is
the lead institution with contributions from all other collaborating
institutions.

![](media/image41.wmf){width="5.995833333333334in"
height="3.3361111111111112in"}

Figure 7.2. Dark Energy Survey Data Management Work Breakdown Structure

Below we provide a brief description of each of the work packages.

1.  Project Management and Collaboration (12 FTE-months) - this covers
    the management of the project.

2.  Software Development Framework (6 FTE-months) - system and policies
    for developing and testing the software.

3.  Software Repository (1 FTE-month)- software revision control system.

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1.  Scheduling and Observing Tools (12 FTE-months) - graphical user
    interface that is primary user interface with camera (and
    telescope). Includes interface with survey database to help in
    scheduling observations and includes monitors of a range of camera,
    telescope and data quality characteristics.

2.  Archiving and Data Transfer (4 FTE-months) - deploy tools to enable
    internet (and backup option) data transfer from the mountain to the
    collaboration sites.

3.  Automated Quality Assessment (4 FTE-months) - tools to automatically
    assess data quality. These tools will be deployed on the mountain
    and also at the primary data processing station at NCSA.

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1.  Data Model (2 FTE-months) - definition of data products, metadata,
    and catalog tables.

2.  Standard Collection Access (7 FTE-months) - deploy tools to enable
    data access for the collaboration and the public.

3.  Archive Replication (3 FTE-months ) - tools to replicate (portions
    of) the archive at partner sites.

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1.  Pipeline Processing Framework (9 FTE-months) - grid based framework
    for the automated management and execution of processing pipelines.

2.  Single Frame Calibration Pipeline (12 FTE-months) - tool for basic
    reductions and astrometric and photometric calibration of single
    pointing images

3.  Co-add Pipeline (12 FTE-months) - tool to take calibrated single
    pointing images and combine to build deeper, coadded images from
    which much of the Dark Energy Survey science will be done. Images
    and variance images will be produced.

4.  Source Extraction Pipeline (12 FTE-months) - tool that will create
    catalogs and characterize objects found in the co-added survey data.

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1.  Simulation Interface and Integration (3 FTE-months) - Creation of
    insert interface for mock data used for data challenges, in
    collaboration with simulation team (WBS 1.3.3 and WBS 1.3.4).

2.  Data Challenge I (2 FTE-month) - Interactive survey and testing of
    existing software.

3.  Data Challenge II (1 FTE-month) - Test of archive and pipeline
    framework.

4.  Data Challenge III (1 FTE-month) - Full challenge of data reduction
    using up to a year's worth of data.

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5.  Science Analysis Pipelines (3 FTE-months) - effort tracked within
    the Data Management WBS for integrating science analysis pipelines
    (e.g. photometric redshift catalog pipeline) into the pipeline
    infrastructure.

6.  Survey Operations (216 FTE-months) - data management operations
    during the survey observing phase.

7.  Dark Energy Science Analysis (0 FTE-months) - scientific staff
    effort to analyze and publish the results of the survey.

**7.3.3 Data Management Coordination**

A Data Management Steering Group (DMSG) has been formed to drive
high-level design activities for Data Management. Membership will
include software and science representatives from each partner
institution. (Currently, it includes Plante, Mohr, Annis, Smith, and
Chris Stoughton.) The DMSG is responsible for setting the overall
roadmap for data management, defining system requirements and the
development process, and working out the high-level design.

In contrast to the DMSG, the Computing Working Group (CWG) will be
formed later once the project is up and running. It will be made up
primarily of the software developers and system specialists. (It is
likely the two groups will share membership.) Once the CWG is formed,
much of the design activity will shift to this group as they flesh out
the data management work package designs. They will also handle design
and code reviews as necessary.

**7.3.4 Data Management Project Cost Estimate**

The total estimated manpower requirements for the data management
portion of the project---excluding WBS 2.7 (which covers survey
operations at 216 FTE-months) and WBS 2.8 (covering science analysis) is
112 FTE-months. Our labor model has 0.25 FTE/yr covered by the Project
Manager for Data Management (primarily covering management tasks), 0.25
FTE/yr covered by the Project Scientist, and 3 FTE/yr of programming and
postdoctoral support. Over four years, this level of effort sums to 168
FTE/months, providing 50% contingency on the work package labor
estimates during the build phase of the data management project. In
addition, we will request \$100k to support hardware upgrades (drives)
at NCSA to partially support the requirements for the data processing
and archiving. As shown in Table 7.4, the total estimated cost of the
data management project build phase (not including survey operations) is
\$1.77 million. Of this amount, \$0.57 million is available as an
institutional match through the provision of 1 FTE over the four year
extent of the build phase and 2 FTE-months/yr of salary for the project
scientist. (Note that the cost of maintaining the equipment at NCSA is
an in-kind contribution, estimated to be 0.5 FTE/yr for an additional
\$0.2 million institutional contribution to the project.) The remainder,
\$1.2 million, will be funded through a proposal to NSF.

#### 7.3.5 Data Management Project Schedule {#data-management-project-schedule .unnumbered}

The data management project schedule has the full data management system
tested and in operation at the beginning of 2009, when the camera is
being shipped to the mountain for commissioning. A conceptual timeline
for the major work packages appears in Figure 7.3. The data management
project will begin in earnest in fall 2004 with the hiring of one person
(using U Illinois institutional support) to help Mohr and Plante further
refine the data management design and begin the build phase. In 2009 the
transition to operations will take place, and in fall 2009 the survey
operations at CTIO will trigger full scale data management operations.
These operations will continue for the five year survey and extend one
year beyond the end of the survey. Funding to support these activities
will be requested as the operations phase approaches. At the end of
survey operations there will still be an archive to support. We
currently envision this archive remaining at NCSA indefinitely, perhaps
as part of a new partnership to archive a wide range of NOAO data.
Support for these activities, which we expect to be minimal, will be
sought as the survey operations phase nears an end.

![](media/image44.png){width="5.280708661417322in"
height="2.3035433070866143in"}

5.  **Data Management Reviews**

We are planning three levels of review of the data management effort:
the design and code review process, the data challenges, and a general
review by the Project Advisory Board.

The first two levels occur internal to the Data Management portion of
the project and are overseen by the project manager. The internal design
and code review is the process defined by the Data Management Steering
Group (see sections 6.1 and 7.3.3). The purpose of this process is to
make the design and implementation transparent to the entire
collaboration and to ensure efficient integration of the various
components. The data challenges (sections 6.6 WBS 2.5) are designed to
verify that the system is capable of extracting the target science and
meets the requirements imposed by the expected data rate.

In addition to the internal reviews, we will coordinate with the
Instrument project a series of reviews by the Project Advisory Board, or
by a panel of outside experts invited by the Board, to evaluate our
progress toward a working system.

3.  **Funding and Resource Model**

The funding and resource model that is presented here addresses the
construction and commissioning phases of the project and the initial
operations. We propose that DOE provide the primary funding for the
Survey Instrument project, because Fermilab is the lead institution and
that the NSF provide the primary funding for the Data Management
project, because NCSA is the lead institution. Since all institutions
plan to participate in both projects we plan to request funds from both
agencies for each project. At this time we plan to submit one large
proposal to the NSF for funds for the Survey Instrument and Data
Management projects. We also plan to submit smaller proposals to the NSF
and private foundations for FY2005 funding in order to keep the
development program on track.

We plan to request \~\$5M for the construction and commissioning phases
of the Survey through proposals to the NSF Astronomy and Physics
Division. The purpose of these proposals would be to obtain \$3.4 M
(\~35%) for the M&S costs of the Survey Instrument as well as \$1.2 M
for the Data Management Project. We also plan to make requests to
private foundations for \$1.5 million in order to provide a cushion in
the event our proposals to the NSF fall short of the mark. Should we be
successful in obtaining private funds they would be used to advance the
schedule. Fermilab has recently given us stage one approval (July 2004).
Until NOAO has also approved our proposal, it will be difficult to
engage the NSF in a discussion of the specific details of our proposals
to the NSF. Moreover, we cannot approach private foundations in earnest
until we have received this approval.

We first present the funding model for the Survey Instrument project in
section 7.4.1 and then the funding model for the Data Management project
in section 7.4.2. In each instance, we present the funding model for the
institutional labor costs separately from the funding model for the
materials and services costs.

1.  **Survey Instrument Funding Model**

**7.4.1.1 Model for Survey Instrument Labor Costs**

The collaborating institutions have provided the scientific and
engineering support for the preparation of the Reference Design of the
Dark Energy Survey Instrument from their institutional funding and
existing grants. Our model assumes that these institutions will continue
this support through the construction and commissioning phases of the
project. In particular, it assumes that Fermilab will provide the
scientific, engineering and technical labor for the instrument project
as described in section 7.2.4. The model requires a modest increase in
the Fermilab scientific, engineering, and technical support from within
the laboratory. The estimated Fermilab labor cost of \$7 M is shown in
Table 7.1. Our model assumes that the UIUC HEP group will provide the
scientific, engineering and technical labor for the data acquisition
system. It further assumes that CTIO will contribute to scientific,
engineering and technical effort to the survey instrument project as
part of the partnership arrangement that is mentioned in the
Announcement of Opportunity. This will include the effort to upgrade the
telescope and its controls and to install and integrate the instrument
on the Blanco. The model assumes that the University of Chicago will
continue to support their faculty, graduate students and post-docs
through institutional funds and existing and future grants. It further
assumes that the University of Illinois Astronomy Department will
continue to support its faculty when they work on the Survey Instrument
project.

We will actively seek additional scientists and technical staff from
within the collaborating institutions as well as from new institutions.
We are open to admitting additional groups provided they will bring
additional resources to the construction and commissioning of the
Instrument without increasing the costs of the current participants.

**7.4.1.2 Model for Funding Materials and Service Costs**

The estimated cost of the materials and services for the Survey
Instrument project, after including a contingency of 36% and excluding
overhead, is \$11.45 million. We propose that DOE provide \~65% of the
required M&S funds and that we obtain other funds for the remaining
\~35% of the M&S funds. Table 7.3 presented the funding profile in our
model for obtaining the funds for the base M&S costs. This profile will
allow commissioning to be completed in March 2009, whereas we believe
that we could complete commissioning in October 2008 if the funding
profile did not constrain the placement of orders. The cost profiles for
both M&S and labor (excluding institutional overhead) are shown in Table
7.6.

Table 7.6: Instrument costs (then year \$K) based on the funding profile
in Table 1.

  ------------------- ---------- ----------- ----------- ----------- ----------- ---------- ------------
                      **FY04**   **FY05**    **FY06**    **FY07**    **FY08**    **FY09**   **Total**

  M&S                 0          1,097       3,321       3,362       520         68         8,368

  M&S Contingency     0          0           379         338         1,580       782        3,079

  **Total M&S**       **0**      **1,097**   **3,700**   **3,700**   **2,100**   **850**    **11,447**

                                                                                             

  Labor               609        1062        955         1150        560         299        4,635

  Labor Contingency   304        531         478         575         280         150        2,318

  **Total Labor**     **913**    **1593**    **1433**    **1724**    **839**     **449**    **6,953**

                                                                                             

  Total (M&S + Labor) 913        2,690       5,133       5,424       2,939       1,299      18,400

  DOE (UIUC+Fermilab) 913        2,090       3,933       4,224       1,939       1,249      14,350
  ------------------- ---------- ----------- ----------- ----------- ----------- ---------- ------------

The DOE cost to UIUC and Fermilab is the fully total minus the other
contributions shown in table. When overhead is included the total
project cost for the DES instrument is \$22.5M.

**7.4.2 Data Management Funding Model**

The NCSA has agreed to provide the Dark Energy Survey Data Management
operation with 100 TB of disk storage, 400 TB of fast tape storage, and
the CPU hardware platform to process the data. The estimated CPU needs
can be met with a range of machines that can provide a minimal
guaranteed allocation of 10,000 SU/year. Additional CPU needs will be
met through a formal proposal process at NCSA. These resources and their
maintenance will be provided by NCSA at no cost to the project. The
proposed hardware platform should be adequate for routine (normal)
processing and distribution of the data. We expect that Fermilab will
provide the computing equipment that the Fermilab participants will need
to carry out their work related to simulations and archival research,
including sufficient disk and tape storage to contain copies of the
various data products as needed. Compute farms at Fermilab will be
employed as necessary for processing, particularly during full data
reprocessing. We will also leverage the expertise in the Fermilab
Experimental Astrophysics Group (EAG) that is currently dedicated to the
SDSS.

The major new funding need for the Data Management project will be for
additional staff support from NCSA and post docs and graduate students
from the University of Illinois Astronomy Department dedicated to
building the processing system and then processing and distributing the
data to the collaboration and the astronomy community. The University of
Illinois has committed 1.0 FTE (comprised of a 0.5 FTE commitment from
the NCSA director and 0.5 FTE from the Associate Dean of LAS and the
Astronomy Department Chair). During this phase, we estimate that we will
need the support of another 1.5 FTEs plus partial support for the Data
Management Project Manager at the University of Illinois. In addition,
funds at the level of \$100K will be needed for additional disk
purchases at NCSA. Funds are also needed for travel and archival
research. When the support of a post doc and student at the University
of Illinois Astronomy Department is included we estimate that the total
funding need for the period 2005 through 2009 is \$1.2 M (FY04\$). As
noted earlier we will submit a proposal to the NSF that will include a
request for this support in the amount of \$1.2 M as part of a larger
proposal. This part of the request will be submitted to the NSF through
the University of Illinois Astronomy Department.

4.  **Outreach**

The Collaboration will contribute to outreach by connecting with the
established, highly developed education and public outreach activities
at the participating institutions. Fermilab, LBNL, NCSA, and NOAO have
developed extensive programs with teachers and K-12 that present natural
opportunities. In addition, the Collaboration will build a website that
will serve the Collaboration for communication and documentation during
the construction phase and then as the operation phase draws near the
website will have substantial content for the general public as well as
the astronomy community. This website will also be a portal for both the
collaboration and public access to the data and the catalogs. This
approach was used successfully with the Sloan Digital Sky Survey. See
[http://www.sdss.org](http://www.sdss.org/) .

**7.5.1 Websites**

The NOAO website will be a natural way to present the capabilities of
the Survey Instrument and the plans and status of the DES to the NOAO
community. Indeed this is already being done. We envision a partnership
with NOAO through their office of public outreach in all aspects of
public outreach and education. The DES Collaboration could contribute
material for the pages dedicated to the DES on this website as well as
to other appropriate pages. We propose that the dedicated DES pages will
have content that describes the DES Survey Instrument and the DES
Science Goals in a way that is appropriate for the NOAO communities
(both astronomers and the general public) and it will have links to the
DES website, the NCSA website and websites of the other participating
groups. Once commissioning begins the NOAO website will be the place to
show schedules for operations and links to the portals for the data and
catalogs.

We propose that the collaboration create special DES pages for the NCSA
website that will contain the status of data processing operations and
software development as well as descriptive material that describes the
DES and the NCSA role in the DES. Since the primary location of the data
and the catalogs will be at NCSA this website will provide information
on how to access the data and catalogs and it will provide the primary
access to the data and the catalogs. These web pages will have content
that describes the DES is appropriate for the general public and the
special communities that use NCSA.

The DES collaboration plans to develop a website at Fermilab for its
internal needs in the very near future. While this website will be
initially for collaboration documentation and communications, we will
add content of interest to the general public and the scientific
community. This content will have a particular emphasis on the
connections among particle physics, astrophysics and cosmology. The
website will be accessible to the public although the portions of the
website that will contain documents that describe collaboration work
that is in progress will only be accessible to the collaboration through
passwords. The public portion of this website will present the status of
the construction of the Survey Instrument and after data taking begins
it will provide the same information on the data and catalogs as will be
on the NCSA DES web pages. Since Fermilab will be a mirror site for the
data and catalogs this website will also be a portal to the data and
catalogs. This approach was used very successfully with the SDSS
website, which is currently maintained by Fermilab. We quote from a
brief description of the SDSS website here.

> "The SDSS collaboration is engaged in a broad outreach effort that
> reflects the extended geographic distribution of the participating
> institutions and the participants. The project team at Fermilab
> maintains two publicly accessible, closely related websites,
> <http://www.sdss.org/> and <http://skyserver.fnal.gov/en/>. One of the
> objectives of these websites is the communication of the exciting
> discoveries in astronomy, astrophysics, and cosmology that are being
> made with the SDSS Archive to the public. They serve readily
> understandable, popular materials about the SDSS that are designed to
> appeal to people of all ages interested in the Universe. Both also
> provide access to the public SDSS Archive, a rich resource that the
> astronomical community uses for research. The SkyServer specializes in
> presenting written and graphical material to K-12 and college students
> and their teachers on current developments in astronomy, astrophysics,
> and cosmology. The SDSS website provides popular descriptions of the
> SDSS telescope and instruments, the goals of the survey, and the
> Apache Point Observatory, the home of the SDSS telescopes, for the
> layperson. It also displays the recent press releases and news
> articles describing research results obtained by the SDSS
> Collaboration. The SDSS web site displays the image of the week taken
> from SDSS observation and it provides the status of the survey,
> information on research topics submitted for publication, and access
> to the Archive, thus enabling the public to follow the progress of the
> survey."

We propose to build a DES website that is very similar to the SDSS.org
website for the collaboration and we will request funds to partially
support the development of the educational content for a website that is
similar to the Skyserver. At this time we have not decided on whether to
host the Skyserver like website at NCSA, NOAO or Fermilab, although we
believe that this development work could be best done if it were done
through one of the education efforts at NOAO, NCSA, or Fermilab. We
anticipate that the request for funds will be made through a separate
proposal jointly submitted by the interested education offices.

**7.5.2 Education**

All of the participating institutions are engaged in educational
activities and we propose to contribute to their existing activities
with material that is specific to the DES. Fermilab, LBNL, NCSA, and
NOAO have educational outreach activities that focus on K-12 students
and teachers. Except as noted in the websites above we propose that each
DES group contribute to the activities associated with its institution.
Thus the Fermilab groups would contribute to the K-12 educational
activities at Fermilab, the University of Illinois groups and the NOAO
groups would contribute to similar activities at the NCSA and CTIO and
Tucson respectively. It may be appropriate for these offices to submit a
joint proposal for a program that ties astrophysics, cosmology and
particle physics together for K-12 students and their teachers.
Alternatively they could make a significant addition to a program such
as Quarknet that introduces the connections between the early universe
and particle physics. In any event such efforts would require additional
funding and this funding is best managed by the education offices
working together with help from the DES collaboration. Once the DES is
approved and secure funding profile has been obtained the DES
collaboration will work with the education offices to determine whether
these possibilities can be developed.

**7.5.3 Public Information**

When it is necessary to prepare press releases and other formal
statements to announce important accomplishments we propose to do this
through institutional contact persons with the offices of public affairs
at each institution. Initially a lead public affairs officer will be
designated to coordinate such public information activities among the
participating institutions, the funding agency representatives, and the
media. This arrangement worked quite well for the SDSS, although once
operations became routine and science publications being routinely
released a public affairs consultant was hired to manage the day-to-day
work.

**7.5.4 Existing Outreach Activities**

Some members of the DES Collaboration are already involved in public
outreach as noted in the following:

-   The U Illinois Physics Department runs a Saturday Physics program
    for the local community. It is aimed at HS students, but everyone is
    welcome. See <http://www.physics.uiuc.edu/outreach/Honors/> .

-   Mats Selen, University of Illinois Physics Department, has a weekly
    TV show on the local CBS station, and the videos are available on
    the web. See <http://www.hep.uiuc.edu/home/mats/whysguy.html> .

-   Tom Droege is the creator of The Amateur Sky Survey which is
    described on: <http://www.tass-survey.org/>. One of the outreach
    programs was a science fair project. A technical description is at:
    <http://www.tass-survey.org/tass/tass.shtml> And a recent write up
    on TASS can be found at

> <http://www.tass-survey.org/tass/showtell/st0010.html>

The Amateur Sky Survey is a unique feature of the DES and we would like
to use the DES website and perhaps the NOAO website to make it more
readily available to the amateur astronomy community.

**7.6 Relevant, Prior Experience of the Dark Energy Survey
Collaboration**

**7.6.1 Instrument Construction**

While the construction of a wide-field camera will be a new challenge
for Fermilab, the Dark Energy Camera team includes scientists and
engineers who have built state of the art silicon vertex detectors.
Vertex detectors use many of the technologies and technical skills that
are directly relevant for the construction of wide-field cameras. The
accurate placement of sensors in a silicon vertex detector has important
similarities with the accurate placement of CCDs on a focal plane and
SiDet has superb facilities to build a focal plane. The Fermilab group
at SiDet, which has extensive experience in wire bonding. These
techniques can be adapted to bonding the camera front-end electronics
onto the CCDs. In addition, CCD sensors share a common technology base
with the silicon vertex detectors sensors. As noted earlier, the
Fermilab and LBNL groups are exploring the use of the thick, high
resistivity CCDs in the Dark Energy Camera. The LBNL groups have
developed state of the art CCDs of this type, including their front-end
electronics, for applications in astronomy over the past five years.
Devices very similar to the devices specified in the Camera Reference
Design have already been successfully deployed in telescopes at Kitt
Peak. However, the use of the 2K x 4K LBNL CCDs in our proposed camera
will require the creation and evaluation of a new mask for the
production vendors. The Fermilab and LBNL members of the Instrument team
have developed a preliminary plan to do this. In the past, Fermilab and
LBNL have worked closely to develop several generations of the SVX chips
and silicon strip vertex detectors for the current CDF and DZero
experiments and thus it is natural for the Fermilab and LBNL
participants to explore a similar partnership for the Dark Energy
Survey.

**7.6.2 Data Management**

The University of Illinois Astronomy group has extensive experience in
analyzing and archiving large optical and radio astronomy datasets. NCSA
provides basic data management for all users of NCSA computing
facilities across a variety of fields of science and engineering.
Plante, as part of the NCSA Radio Astronomy Imaging group, has led the
development and operation of the NCSA Astronomy Digital Image Library,
the BIMA Data Archive and Image Pipeline. The astronomy group also has
extensive experience in distributed (cross-institution) software
development. In the early 1990s, NCSA was a partner in the development
of the Miriad software system for radio interferometers. It was a
founding member of the AIPS++ consortium, and it is currently a
collaborator in the CARMA (Combined Array for Research in Millimeter
Astronomy) Software Development project. Along with Fermilab, it is a
collaborating institution in the National Virtual Observatory (NVO).

The Fermilab participants from the Experimental Astrophysics Group (EAG)
have gained extensive experience with data management of large surveys
through their fourteen-year participation in the SDSS. In particular,
they contributed to coding and debugging the pipelines for the SDSS,
they operate the data processing and public data distribution center for
the SDSS, and they are responsible for a major part of performing data
quality assurance on the processed SDSS data. The EAG was very active in
the creation of the SDSS archive and it manages and operates it.

It is from this extensive experience in data management and distributed
software development that our data management plan has been drawn and
gives us confidence that we can meet the DES requirements for data
management and place this archive in the NVO.

**7.6.3 Data Analysis and Interpretation**

The members of the Fermilab Theoretical Astrophysics Group and the
University of Chicago participants have made important contributions to
the interpretation of the data from large surveys in the framework of
the standard model of cosmology. The Fermilab Theoretical Astrophysics
group was the first group at a DOE National Laboratory to specifically
set out to explore the connection between the early universe and
particle physics. Both groups have made important contributions to the
interpretation of large-scale structure (galaxy), cluster counts, and
weak lensing, and all of the analysis techniques that will be used to
achieve the scientific goals of the Dark Energy Survey. Moreover, with
members of the EAG, they have been active in applying these techniques
to the analysis of SDSS data. The EAG participants also have experience
in the analysis of wide-field multi-color surveys such as SDSS and
CNOC2. The Fermilab particle physicists plan to contribute to the
science analyses.

University of Illinois scientists have focused on cosmology, structure
formation, quasars and galaxy formation using data from X-ray
satellites, optical observations, interferometric SZE observations and
large scale, near-infrared surveys carried out by 2MASS and at Kitt Peak
National Observatory using the FLAMINGOS camera. These analyses have led
to important contributions to our concordance model of cosmology through
precise constraints on the matter density from cluster baryon fraction
measurements. Scientists at U Illinois and their collaborators have
introduced and further developed the use of galaxy cluster surveys for
the study of dark energy. In addition, one Illinois scientist is
involved in the data analysis and interpretation of the DPOSS optical
plate survey and the QUEST2 time domain CCD survey.

The LBNL Cosmology Group established a strong reputation for developing
the successful observing strategies that allowed them to discover large
numbers of supernova and follow their light curves. Their subsequent
analysis of this data helped to establish some of the basic parameters
of the concordance model of cosmology. The CTIO Dark Energy Survey
participants are members of an independent group that developed similar
observation strategies and analysis techniques for discovering and
understanding supernovae. That group has achieved similar results of
great importance. The two competing teams independently discovered the
accelerating universe, perhaps the most profound discovery in cosmology
in the past two decades and have joined forces in the DES Collaboration.

**8. The Relationship of the Dark Energy Survey to other Astrophysics
Projects**

In the course of preparing this proposal we gave considerable thought to
the place that the DES has in relationship to ongoing projects and the
more ambitious future projects with a longer time scale such as JDEM and
LSST. We prepared section 8.1, a roadmap for a U.S. Dark Energy Program
in order to place the DES in the context of the extended program that we
believe must be carried in order to understand dark and dark matter. We
consulted the leaders of the more ambitious projects while we prepared
the roadmap, and there is agreement that it would give the U.S. a superb
program. However the dates that we used for each project are our
estimates of how long it will take to secure the funds. We did this to
make the assumptions for approval similar. Section 8.2 describes the
current commitments of the collaboration members to other astrophysics
projects and their temporal relationship to the Dark Energy Survey.

1.  **A roadmap for A U.S. Dark Energy Program**

What is the nature of the Dark Energy? The National Research Council
Report, *Connecting Quarks With the Cosmos (2002)*, identified the dark
energy as one of the most profound questions about the Universe that are
ripe for critical progress in the coming years. The OSTP interagency
Physics of the Universe report pegged dark energy as the highest
priority area for research. More recently, the Director of the Office of
Science and the Assistant Director for Mathematical Sciences of the NSF
requested that HEPAP identify the key questions now faced by high energy
physics, particle astrophysics and cosmology. Their response, *The*
*Quantum Universe*, stated that the answer to the question "How can we
solve the mystery of dark energy" is crucial for progress in these
fields.

Dark Energy became a central issue, in 1998, when two independent groups
studying distant supernovae discovered that the expansion of the
Universe is accelerating. Subsequent observations of the cosmic
microwave background radiation and of the large-scale distribution of
galaxies confirmed and amplified this finding. According to Einstein\'s
General Relativity, if the Universe is filled with ordinary matter,
gravity should be slowing down the expansion. Since the expansion is
speeding up, we are faced with two possibilities, either of which would
have profound implications for our understanding of the cosmos and of
the fundamental laws of physics: either two thirds of the energy density
of the Universe is in a bizarre new form called Dark Energy, or General
Relativity breaks down on cosmological scales and must be replaced with
a new theory of gravity, perhaps associated with extra spatial
dimensions. In both cases, the first-order effects on the expansion of
the Universe can be described by an effective fluid with negative
pressure, *w* = *p/pc^2^* \< -1/3; we will therefore follow common
practice and subsume both possibilities under the general rubric of Dark
Energy. For example, the dark energy could be the energy of the quantum
vacuum, that is, Einstein\'s cosmological constant (in which case *w* =
-1), or it could signal the existence of a new ultra-light particle with
mass of order 10^-33^ GeV/*c*^2^ or less. In either case, particle
physics theory currently provides no understanding of why the dark
energy density should have the value that would explain the present
acceleration of the Universe.

In order to pin down the nature of the dark energy and decide between
the theoretical alternatives, we must probe the dark energy (and measure
the parameter *w*) with greater precision and determine whether and how
it evolves with cosmic time. Here we briefly lays out our vision for the
elements of a coherent Dark Energy Program aimed at achieving those
goals over the next decade. It consists of a sequence of logical,
incremental steps of increasing scale, technical complexity, and
scientific reach, culminating in what should be definitive measurements
and, as we expect, a breakthrough to a new paradigm. Successful
execution of the program will rely upon interagency cooperation, upon
collaboration among national laboratories, agency-supported centers, and
universities, and upon public-private partnerships. *Connecting Quarks
with the Cosmos* offered a bold vision for addressing the mystery of the
dark energy; the program described herein describes a plan for
implementing that vision. Key elements of this vision have been endorsed
and amplified in the Physics of the Universe report, the *Beyond
Einstein* program at NASA, the HEPAP subpanel report, the *Quantum
Universe*, the DOE Office of Science Facility Plan, and the Astronomy &
Astrophysics Decadal Report.

From supernovae (Supernova Cosmology Project and High-z Supernova
Search), the CMB (most recently WMAP), and large-scale galaxy clustering
(the Sloan Digital Sky Survey (SDSS) and the 2 Degree Field Survey
(2dF)), the dark energy parameter *w* is currently determined with
approximately 15% uncertainty (1-sigma), and we have essentially no
constraints on its time evolution. Note that this level of uncertainty
is currently only achieved when combining these different probes
together and when strong priors are assumed on other cosmological
parameters: currently, any single method delivers at best 30%
constraints on *w*. The Dark Energy Program is aimed at sequentially
increasing the precision of *w* measurements to the few percent level
and constraining its time evolution at the \~ 25% level, with little or
no prior constraints on other parameters. It is important to emphasize
that the level of uncertainty described here and below does not yet
include full accounting for systematic errors in all cases. Moreover,
forecast constraints for dark energy parameters generally depend on
input assumptions about the time evolution of the dark energy (the
numbers here and below assume constant *w*) and about how well the
values of other cosmological parameters are determined. As a result,
extreme caution must be exercised in comparing the projected dark energy
sensitivity of different experiments and methods.

Experimentally, we probe dark energy by studying the impact it has had
on the history of the expansion rate of the Universe. Through the
expansion rate, the dark energy affects observables such as the apparent
brightnesses of standard candles (such as supernovae), the apparent
sizes of standard rulers, the volume of space containing \`standard
structures\' such as galaxies and clusters of galaxies, and the rate at
which those structures form. In recent years, a number of very promising
new methods for probing dark energy have been developed. Since each of
them measures different combinations of these observables, with
different systematic errors and with different dependences on the
cosmological parameters, they are doubly complementary. Because the
nature of the dark energy is such an important question, and the
measurements technically challenging, the most promising complementary
dark energy probes must be pursued in order to form a robust program.

Four techniques hold great promise to us as probes of dark energy: (i)
the luminosity distance (apparent brightness) of Type Ia supernovae,
(ii) weak gravitational lensing of distant galaxies and its dependence
on redshift, (iii) the redshift distribution and clustering evolution of
galaxy clusters, and (iv) the evolution of the spatial clustering of
galaxies, using features in the galaxy power spectrum as standard
rulers. While this list does not exhaust the potentially powerful probes
of dark energy (which include, e.g., the integrated Sachs-Wolfe effect,
lensing of the cosmic microwave background, the Lyman-alpha forest
cross-power spectrum, and strong gravitational lensing statistics),
given our current knowledge it constitutes the minimal set that together
has the best opportunity to achieve the aims of the program. Of the four
techniques, supernovae are the furthest advanced as a dark energy probe
and the method for which systematic errors have been most thoroughly
characterized. All these techniques benefit from complementarity with
cosmic microwave background (CMB) measurements: while the CMB anisotropy
itself does not strongly probe the nature of the dark energy, it
provides important constraints on other cosmological parameters that
enhance the dark energy reach of these methods. The strongest CMB
constraints currently come from the WMAP satellite; later in this
decade, the Planck Surveyor and ground-based CMB polarization
experiments will provide even more powerful complementary constraints.

A number of surveys are underway with the aim of determining *w* with
10-15% statistical uncertainty (when combined with the CMB) by about
2008. The ESSENCE survey at Cerro Tololo Inter American Observatory
(CTIO) in Chile and the CFHT Legacy Survey on Mauna Kea, both on-going
5-year projects, will measure distances to many hundreds of Type Ia
supernovae to redshifts *z* \~ 0.8, vastly increasing the dataset over
this range. The Hubble Space Telescope, using a small number of
supernovae found at *z* \> 1, is providing a first glimpse of the
transition from an early decelerating phase of expansion to the present
accelerating phase. The Nearby Supernova Factory, starting in 2004, will
provide multi-epoch spectrophotometry for a few hundred low-redshift
supernovae, increasing the cosmological precision of these high-redshift
supernova measurements and probing systematics. The proposed extension
of the SDSS will augment these surveys by measuring 200 supernovae in
the redshift desert from *z* = 0.1 to 0.3. Using weak lensing
measurements covering on the order of 100 square degrees, the on-going
DeepLens and CFHT Legacy Surveys, among others, will provide the first
constraints on dark energy from this technique.

While these projects are exploiting the supernova and weak lensing
methods in the near term, these methods will be further developed in the
intermediate term and augmented with the cluster counting approach to
dark energy. Several projects, including APEX, the Atacama Cosmology
Telescope, and the South Pole Telescope (SPT), will carry out
increasingly large cluster surveys using the Sunyaev Zel\'dovich effect.
This effect is powerful because it can be used to find clusters at all
redshifts and provides a robust estimate of the mass of each cluster.
The South Pole Telescope, slated to begin operations in 2007, will find
and measure \~ 30,000 galaxy clusters in a survey covering 4000 square
degrees. The Dark Energy Survey (DES), a proposed five-year survey that
could begin in 2009, will use a new wide-field camera to be built for
the existing Blanco 4-meter telescope to cover the SPT survey area in
four optical passbands, providing the needed photometric redshifts for
these clusters. Together, SPT+DES will aim to deliver \~ 5-10%
statistical accuracy on *w* using the cluster counting method (and no
priors) and will develop this method as a precision cosmological probe.
The Dark Energy Survey will also constrain the dark energy with similar
statistical accuracy via weak lensing, the evolution of galaxy
clustering, and supernovae. On a similar timescale, the DUO survey, a
proposed NASA MIDEX mission, if approved, would carry out an X-ray
census of clusters with substantial dark energy reach. These
intermediate-term projects will take the next major step in probing dark
energy and will begin to seriously constrain its time evolution. They
will also help explore the limitations of the different techniques. This
knowledge will be useful for and help ensure the success of the more
ambitious and more challenging projects to follow.

For the longer term, *Connecting Quarks with the Cosmos* and *Physics of
the Universe* recommended that ground- and space-based wide-field
telescopes with Gigapixel-scale cameras be pursued to address the dark
energy question. They highlighted two major complementary projects that
have been proposed with primary goals that include probing the dark
energy. The Large Synoptic Survey Telescope (LSST) is a proposed large
ground-based telescope with a wide-field imager that will carry out
multiple scans over roughly half of the sky; it will study a variety of
transient astronomical phenomena and will probe dark energy via weak
lensing cosmic shear, lensing cluster counts, and galaxy clustering. The
Supernova/ Acceleration Probe (SNAP) is a satellite concept for the
NASA-DOE Joint Dark Energy Mission that will include a large survey of
\~ 3000 supernovae that reaches redshifts *z* \~ 1.7 and a wide-area
survey to measure weak lensing, optical cluster counts, and galaxy
clustering. LSST is designed to make optimum use of the cosmological
information that can be gleaned from ground-based imaging, while SNAP
will be uniquely suited to measure high-redshift supernovae with optical
and near-infrared detectors on a stable platform above the atmosphere.
These projects aim to achieve exquisite precision on cosmological
parameters using these complementary techniques, with statistical error
of a few percent on *w*. Moreover, they have sufficient reach to offer
the real possibility of determining the time evolution of the dark
energy. Since they are being optimally designed *ab initio* to achieve
these goals, these projects should have unprecedented control of
systematic errors. Given the complementarities of the dark energy probes
they will pursue and the systematic uncertainties associated
particularly with the newer dark energy methods, both projects are
required in order to achieve the goals of a robust dark energy program.
These projects aim to begin science operations about a decade from now.
In addition, a deep, wide-area spectroscopic survey, using a Wide Field
Fiber-fed Multi Object Spectrograph, similar to the KAOS concept that
was recently proposed for one of the Gemini telescopes, or a space-based
instrument, could further exploit the galaxy clustering dark energy
probe during this time period.

In sum, the Dark Energy program outlined above encompasses a coherent,
graded approach to one of the great unsolved mysteries of science. The
timeline of the program proceeds sequentially from the current surveys
(covering roughly the period 2004-2008), aiming at 10-15% precision on
the dark energy equation of state w, through the intermediate term
(2009-2014), when SPT+DES should deliver 5-10% precision on *w* and
begin to constrain its time evolution through SZ cluster counts and
other probes, to the long term (after 2013), when LSST and SNAP begin to
make the definitive measurements, reaching few percent precision on *w*
and determining its time evolution. Proceeding along the timeline, the
scientific goals of the program become increasingly ambitious, requiring
an increasing scale of effort that builds upon the steps taken before.
The ultimate aim of the program is to fully exploit these complementary
dark energy probes and thereby unravel one of the great scientific
mysteries of our time.

**8.2 The Relationship of the DES to other projects of the DES
participants**

Section 8.2 describes the current commitments of the collaboration
members to other astrophysics projects and their temporal relationship
to the Dark Energy Survey.

**8.2.1 Fermilab Projects**

The Fermilab EAG has had a major involvement in the SDSS over the past
fourteen years. While the approved five-year observation phase of the
SDSS will end July 1, 2005, the SDSS consortium has developed a plan for
SDSS II, a three-year extension and is actively seeking funds for the
extension. The Fermilab Director has approved Fermilab's continued
participation in SDSS II at a reduced level of effort. Since the SDSS
has been in operation for five years its operation is mature, and the
demands on the Fermilab staff can be reduced without hurting SDSS or
SDSS II. The Fermilab astrophysicists in the EAG who plan to participate
in the DES have already reduced the level of their contributions to the
SDSS infrastructure. Several Fermilab particle physicists have joined
the SDSS supernova campaign, the element of SDSS II that is focused
searching for type Ia SN in the red shift range between .10 and .30, a
range in which the SDSS is uniquely qualified. They are already active
participants in planning the campaign, which will begin in the fall of
2004. They will also share the load of infrastructure work with the EAG
astronomers who plan to continue to work actively on the SDSS extension.
Fermilab will support the data processing and archiving of the SDSS
imaging and spectroscopic data, and it will continue to manage
distribution of the full SDSS archive during the extension. The Fermilab
participation in the SDSS will end in July 2008 if all of the funding
for the extension is secured, and if the funding is not secured it will
end earlier. Whenever that happens, the computer professionals and the
astrophysicists in the EAG will be in a position to contribute to the
operations phase of the Dark Energy Survey as well as to carry out
research with the DES archive. After SDSS II ends the Fermilab
scientists working on SDSS II plan to contribute to DES. The EAG
computer professionals have the experience to support simulations,
observations, data processing, data analysis and archiving for the DES.
The DES does not plan to make large demands on the EAG support team,
because the data management and data distribution effort will be led by
and primarily supported by the University of Illinois.

The EAG astrophysicists and several Fermilab particle physicists have
formed the Fermilab SNAP group. All are engaged in contributing to the
SNAP collaboration development of the science case for JDEM/SNAP. The
wide area survey part of SNAP, which includes both weak lensing, and
cluster counts, is of particular interest to the Fermilab DES
participants, who are also engaged in SNAP. They believe that this
effort will speed the development of DES simulations and refine the DES
observing strategy. Since the Joint Dark Energy Mission (SNAP) will
probably not be launched until after 2014, the Dark Energy Survey will
fit nicely in the temporal gap between the end of the SDSS and the start
of JDEM.

**8.2.2 University of Illinois Projects**

One of the University of Illinois astronomers (J. Mohr) is a member of
the South Pole Telescope (SPT) Collaboration. Mohr's responsibilities in
SPT are to secure the optical followup of the SPT cluster survey region
and to improve cluster finding and characterization methods in the SZE
and optical. These responsibilities are obviously well aligned with the
DES. In addition, Mohr is co-I on the Dark Universe Observable (DUO), a
NASA Small Explorer mission in Phase A study. If approved, that mission
will carry out a large X-ray cluster survey to study the nature of the
dark energy. A portion of the DUO survey will overlap the SPT/DES region
near the south galactic cap, and this overlap will enable additional
science such as SZE+X-ray distances and more robust determination of the
evolution of cluster mass-observable relations. DUO will launch in 2008,
if selected. Mohr's responsibilities include refining X-ray and optical
cluster finding techniques, which is an activity that is complementary
to his DES and SPT work. In addition, Mohr will continue to work on
refining techniques for studying the dark energy with galaxy clusters
and the clustering of galaxies. Mohr and collaborators introduced this
technique, writing the first paper on how one can use large cluster
surveys to study the dark energy, and he has continued to develop these
ideas---introducing the concept of self-calibration into cluster
surveys. This work has led directly to cluster survey work with the
Sunyaev-Zel'dovich effect Array (SZA, PI Carlstrom/U Chicago), the SPT
(PI Carlstrom/U Chicago), DUO (PI Griffiths/CMU) and now the DES.

NCSA is supporting a series of astronomical archive and pipeline
processing projects intended to develop the necessary infrastructure to
handle the largest telescopes of the next decade, including LSST. DES
data management project manager Ray Plante (NCSA) continues his work on
archiving and data management with the expansion of the
Berkely-Illinois-Maryland Array (BIMA; mm-wave astronomy array) into the
Combined Array for Mm-wave Astronomy (CARMA). The DES Data Management
plan leverages off of the existing work done on BIMA/CARMA project,
particularly in the area of grid processing. Plante also leads the NCSA
team in an emerging collaboration with NOAO to address distributed
archiving and processing for NOAO data streams. Plante is also currently
leading the project planning for the NCSA LSST effort in advance of a
new NCSA LSST lead to arrive by Fall 2004. In addition to their
participation in the LSST project, NCSA is also a collaborating
institution in the Square Kilometer Array, a radio telescope on a
similar data and time scale as LSST.

All of the University of Illinois DES members plan to participate in
LSST. In particular, the University of Illinois, in partnership with the
NCSA, plans to be a major data processing and archiving center for LSST.
In addition, the software for the LSST data acquisition will be
developed by Thaler's group in the Physics Department. As noted earlier,
this relationship will strengthen the Dark Energy Survey Collaboration.
The needs of the two projects and their schedules nicely match the
availability and interests of the scientists and technical staff at the
University of Illinois.

3.  **University of Chicago Projects**

The University of Chicago members of the Dark Energy Collaboration are
active in several astrophysical projects. Josh Frieman, who is the DES
liaison with the University of Chicago Astronomy and Astrophysics
Department, is a member of the SDSS, and coordinated the development of
its spectroscopic pipeline software. He is co-chair of the SDSS Large
Scale Structure Working Group and is active in the analysis of
large-scale structure and weak lensing data. In addition, he serves on
the SDSS Collaboration Council and is the Fermilab representative on the
SNAP Institutional Board. He has also been engaged in unrelated
observation projects at CTIO in the past. John Carlstrom is the PI of
the South Pole Telescope Project and the leader of the SPT
collaboration, which includes the UC Berkeley /LBNL Cosmic Microwave
Background Group. He will be the primary contact between the SPT
Collaboration and the Dark Energy Collaboration. In this capacity, he
will help to define the procedures for analyzing the relevant data sets
created by the two surveys.

4.  **CTIO Projects**

The CTIO participants are actively engaged in the ESSENCE Project, which
will detect and follow up on \~200 supernovae that will be observed with
the Blanco telescope. The ESSENCE Project is expected to be complete in
the next 3 years. They are also pursuing the observation of nearby
supernovae, because these observations will tie down the Hubble diagram
at zero redshift. They are also engaged in the use of the Monsoon system
with the NEWFIRM Camera. These efforts will help the Dark Energy Survey.

5.  **LBNL Projects**

The LBNL Cosmology Group is engaged in three projects: the Supernova
Cosmology Project (SCP), the Nearby Supernova Factory (SNF), and SNAP.
The Supernova teams are working with the CFHT Supernova Legacy Survey
using Megacam. The goals of this observing campaign are similar to the
ESSENCE Project. The Nearby Supernova Factory will discover \~300 nearby
supernovae with the NEAT telescopes and follow them up with new
instruments on the Hawaii 2.2-meter telescope. Both the SNF and the CFHT
Supernova Legacy Survey are expected to be completed in the next 4
years. LBNL is the lead institution for the proposed SNAP mission.

**9. Community Science with the DECam and the DES Legacy Archive**

The Mosaic Cameras installed at the NOAO 4m telescopes, north and south,
are the most popular facility instruments at NOAO. Over the last two
years, the CTIO 4m imager has been assigned 42% of the total nights
available. The present Mosaic imagers cover a 0.36 sq-degree field at
prime focus in an 8Kx8K format, with a 100s read time. These optical
imagers are the largest field imagers routinely available to US
astronomers, and they support a wide range of highly-ranked science,
such as planet searches, trans-Neptunian object surveys, maps of
emission line regions in the Milky Way, searches for tidal streams in
the Galactic Halo, microlensing in the LMC, inter-galaxian populations
in nearby galaxy clusters, QSO statistics, weak lensing studies, galaxy
counts at redshifts out to z=2, searches for galaxy clusters, and
cosmology with supernovae, to name a few projects.

The assigned time to the present Mosaic projects range from very large 5
year survey projects to much smaller 1-2 night pointed surveys, and we
expect that DECam will be used in the same manner. The light grasping
power of a 4m telescope is ideal for finding objects to be followed up
on 8m telescopes either spectroscopically or in the near-infrared. These
Mosaic imagers are often the first step in any scientific project
requiring Gemini or Keck time. Because the suite of 4m and 8m class
telescopes will not change significantly in the next 10 years, data
products provided by 4m optical mosaic surveys will continue to be
extremely important for the support of the largest aperture telescopes,
as well as for stand-alone photometric projects.

The DECam, with a field 8 times the size of the present Mosaic cameras,
and with a read time of only 17 seconds, will provide a much wider field
and more efficient use of observing time. The initial filter suite will
be the SDSS \"griz\" filters. Any present Mosaic filter can be put into
the filter bolt (during the day) so that all present Mosaic projects can
be continued with these filters. We imagine, however, that a number of
other filters that use the full field will be constructed for important
survey projects, funded outside of the present proposal. We anticipate
that the user community will want to use the interference filters DDO51,
an Hα set, \[OIII\], \[SII\], and Y (1 micron). Less expensive filters
that users may need are the glass versions of the broadband BVR filters.
However, we expect that the popularity of Johnson/Kron-Cousins filters
will steadily decline over the next decade as more programs make use of
SDSS databases.

In the following sections, we give a flavor of the wide range of science
projects that will use the larger field and reduced read time of the
DECam. As a regularly scheduled NOAO instrument, we expect that time on
DECam will be assigned to projects in a similar fashion to the present
Mosaic imagers - serving large surveys allotted time through the NOAO
Survey TAC, as well as the traditional few night runs.

**9.1 Stellar Science Cases**

With DECam, CCD imaging moves further into the field of view regime
previously accessible only through Schmidt cameras and photographic
plates. For example, the CTIO 0.9-m Schmidt, now retired, has a field of
view of 5 degrees by 5 degrees. Such cameras have provided the basis for
many directions of astronomical research, not least the exploration of
populations and distributions of stars in our own and nearby galaxies.
Such stellar astronomy is an obvious field in which DECam will find
considerable use among the community.

**\-- Kuiper Belt searches**

The Kuiper Belt is a region beyond Neptune containing tens of thousands
of small bodies in orbit around the Sun. The KBOs allow us to study how
dust grains in the early solar nebula coagulate into the planetesimals
of the present epoch. The KBOs are fossil remnants of the chemical and
dynamical processes that occured in the outer solar disk at the time the
Solar System formed. Their physical characteristics should tell us much
about the formation of the circumstellar dust disks we see in external
stars and directly tell us about the conditions of planet formation. A
deep survey of the ecliptic will allow us to catalog a population of
thousands of KBOs in terms of their dimensions, content, and dynamical
characteristics. The statistics of such a survey will allow us to better
understand the physical conditions at the time of the formation of the
Solar System. A large number of KBOs will be available for spectroscopic
follow-up at 8m class telescopes. Their thermal and bolometric
properties can be followed up with SIRTF and ground-based mid-infrared
imagers.

**\-- Proper motion surveys of galactic open clusters**

Recent years have seen considerable progress in the identification of
very low mass stars and brown dwarfs, particularly in open clusters.
These systems contain stars of known distance, age and metallicity and
make excellent laboratories for the study of stellar structure over the
full mass range of stable, hydrogen burning stars. It is now possible to
study the physics of coeval objects having masses that range over three
orders of magnitude. Particularly interesting are the newly identified
very low mass stars providing important insight into the study of
lithium evolution, angular momentum evolution, spotting, variability,
coronal activity, binary fraction and mass function. If one assumes that
all disk stars originate in open clusters, then open cluster studies are
relevant to the general field population

Since open clusters subtend generally large angles on the sky, the
majority of work done on them has historically been via Schmidt plates.
Membership is established through a number of means, but proper motion
studies are prominent. DECam will be an ideal tool with which to pursue
future studies, providing faster, more precise and deeper results than a
photographic plate. Of course, this requires that DECam be an
astrometrically stable instrument.

In this field and other explorations of low mass objects, DECam will be
complemented by future wide-field IR instruments, which will permit
confirmation of cluster members. DECam\'s significant red sensitivity
will be particularly useful for observations of the redder, lower-mass
cluster members.

**\-- Cool white dwarfs**

The low-luminosity fall-off and the general form of the luminosity
function of white dwarfs is an important new tool for dating and
exploring the history of star formation in the Galaxy. The coolest white
dwarfs represent the graveyard of the very first stars to have formed in
the Milky Way, now slowly fading into thermal oblivion. It is believed
that these objects are significant contributors to Galactic dark matter,
being found in microlensing surveys such as MACHO+EROS. Finding these
objects is a job for large surveys and DECam is ideally suited.

Such a project may require investment in additional filters beyond those
planned for the Dark Energy Survey, e.g. DDO51 which separates the white
dwarfs which are essentially featureless at 515nm, whereas other stars
generally show significant absorption.

**\-- Globular cluster tidal streams**

Recent years have seen the discovery of streams of stellar debris
emanating from Galactic globular clusters and nearby dwarf galaxies as a
result of the tidal stresses they have experienced in passage through
the three dimensional gravitational potential field of the Milky Way.
Clearly such tails provide invaluable probes of this potential field and
details of its interaction with the source clusters and galaxies.
Because these streams extend over several degrees of arc, or more, DECam
will inevitably see use in their study.

**\-- Variable stars**

Stellar variability in all its forms is an important phenomenon in the
study of stellar astrophysics. From simple binaries, through pulsating
and flare stars, to interacting systems, all aspects of the physics of
stars are open to study, indeed many can only be studied through
photometric variability. DECam, with its fast readout gives it great
potential as the world\'s largest time series photometer. Volume-limited
variability surveys on time-scales of a few minutes is still a
relatively untouched region of the parameter space of ground-based
astronomy and should offer a rich mine for our understanding of the
space densities of numerous types of variable stars and through this, of
stellar evolution in general.

To select a quick example, a single pointing of DECam near the Galactic
plane might be expected to contain as many as several hundred
cataclysmic variables (depending on which estimate of the poorly known
space density one believes) which amounts to a significant fraction of
the number of such systems currently known. A series of short exposures
of such a field will generate a vast number of light curves from which
the cataclysmic variable systems can be identified through their
flickering \-- a stochastic variation on time scales of seconds to tens
of minutes resulting from the interaction of the material accreted from
the late-type star in the system with the accretion disk surrounding the
white dwarf component. It goes almost without saying that the same kinds
of variable survey data will also expose planetary transits and permit
the discovery of new extras solar planets.

**\-- Space astronomy follow-ups**

Finally, ground-based optical surveys have always been vital in the
identification of the sources discovered during space-based surveys in
other regions of the electromagnetic spectrum. In particular, X-ray
surveys provide large numbers of objects which require optical
follow-up. Currently, the Blanco telescope and Mosaic II are seeing use
for following up on the Chandra Observatory surveys, such as the
ChamPlane campaign to identify serendipitous sources in the Galactic
plane, concentrating particularly on X-ray binaries and cataclysmic
variables. Future missions, such as Constellation-X, will have similar
needs, which will be adroitly addressed with DECam.

This is another use of DECam which could benefit greatly from the
availability of filters besides those of the DES. Specifically, X-ray
follow-up and variable star surveys will likely offer some demand for an
H-α filter. Preliminary examination of the current corrector design
implies it is sufficiently telecentric to allow the use of reasonably
narrow filters. Presumably, any filters for DECam use other than for DES
will have to be purchased through third parties and sources such as NSF
proposals, but this is not an unusual method of facilitating a specific
observing program at NOAO.

# 9.2 Extragalactic Science Cases {#extragalactic-science-cases .unnumbered}

**\-- External stellar populations.**

The DECam will be an excellent imager for studies of the stellar
populations of the Magellanic Clouds. With a field of view 2.2 degrees
in diameter, the DECam will cover the main body of each galaxy in a few
pointings. Two science cases can be addressed with the DECam: the
stellar population of the LMC/SMC region and the search for microlensing
in the halo of our Galaxy.

The recent LMC Stellar Catalog by Zaritsky and collaborators done with
the LCO 1m Swope telescope covered 64 sq-degree to a completeness level
of V=20. The DECam can cover the same area in 21 pointings. We
anticipate a community survey project targeting both galaxies and the
extended regions around each galaxy including the bridge between the
galaxies. In uncrowded regions, the photometry can reach m=26 to allow
for searches of faint tidal streams that should be associated with the
multiple passages of both galaxies around the Milky Way. A very deep
search into the possible halos of these galaxies would extend to 10
degrees from the galaxy center. The whole halo of each galaxy could be
covered in 100 pointings with the DECam. Such a survey would be
prohibitive with the present Mosaic imagers.

The SuperMacho program for microlensing of Galactic halo stars on the
screen of LMC stars covers 18 sq-degrees. The SuperMacho program has a 5
year survey mission to find lensing events with 30 half-nights each
year. Each field is revisited every second night in a single color. The
present microlensing search is limited by the field size and read time
of the CCD. The DECam would much more efficiently cover the LMC to
greater depths. The present SuperMacho search expects to find 10-20
events per year with coverage every 2 days using half nights. The same
search could be done in less than 1 hour per night with DECam in two
colors instead of one. With two colors, microlensing (which is
achromatic) can easily be separated from other transients. With coverage
every night for one hour, one could increase the chance of finding
cusping events, which would indicate binary or planetary companions.

As with the Macho and SuperMacho projects, the database for the
microlensing survey provides a rich hunting ground for other projects,
especially those interested in time-varying phenomena.

**\-- Microlensing toward the Fornax Dwarf Galaxy**

The DECam can cover the Fornax Dwarf in a single pointing. A routine
survey of Fornax every few nights over a few months per year with single
pointings will determine the Galactic halo lensing rate toward this
object. With microlensing rates toward Fornax, M31, the LMC, and the
SMC, a spatial distribution of dark compact objects in the Galactic halo
can be determined. Present estimates are that up to 20% of the dark
matter Halo is composed of compact objects, but whether this population
conforms to the flattened spheroid potential of the Halo has not been
determined.

**\-- Dwarf galaxy searches in nearby galaxy groups**

While much attention has been focused on large clusters of galaxies,
most galaxies in the Universe exist in the field or in loose groups,
such as the nearby Sculptor, Cen A, NGC 2997, M66, and M96 groups.
Hierarchical clustering predicts large number of dwarf galaxies of
unknown luminosities in these groups. The number of dwarfs should be
factors of 10 to 100 times the number of brighter galaxies. The search
for these dwarfs requires extremely deep and wide field searches for
very faint red galaxies with spheroidal structure, analogous to the
Milky Way and Andromeda satellite Sph galaxies.

**\-- Intra-cluster light (ICL)**

DECam would be able to image intra-cluster light in nearby galaxy
clusters. This is the very low surface brightness signature of stars
that seem to be associated with the clusters themselves, as opposed to
any one galaxy, though the origin of the stars is an open question: were
they formed along with the cD galaxy or in the intracluster medium, or
are they the remnants of other stripped/disrupted galaxies? Studies of
the spatial distribution and color of the intra-cluster light helps to
pin down the origins.  The distribution of the light can also be used as
a tracer of the halo potential and mass to light ratio.

The DECam would also be an excellent imager for finding PNe in nearby
clusters. These surveys need large apertures and large fields to go very
deep in a \[OIII\]5007 filter, but also cover as much of the nearest
clusters as possible. These tracers could probe even further from the
cluster center than diffuse light to trace the spatial distribution of
the intra-cluster population. The KAOS instrument at Gemini south could
be used to get spectra for the kinematics of the population to compare
with the ICL. In particular, is the ICL population relaxed compared to
the galaxy distribution?

In both projects, the large simultaneous area allows for deep studies of
nearby (and consequently angularly large) galaxy clusters. For the
diffuse light, the simultaneous coverage helps reduce the systematics in
the measurements, which dominate at these low surface brightness levels.
In addition, it is critical to image the cluster with a field
significantly larger than the cluster so that the cluster light can be
measured relative to blank sky

**\-- Photometric surveys of the high redshift universe**

The DECam would be well suited for intermediate depth, smaller solid
angle extragalactic surveys as a complement to the DES. These surveys
could complement ongoing surveys by pushing to larger solid angle than
is possible with current generation cameras. This is especially
important if one wishes to study the clustering of galaxies and its
redshift evolution, and large solid angle, deep photometry is also
critical for any program that seeks rare, faint objects, such as quasars
beyond a redshift z\~6.

**9.3 DES Legacy Archive Science**

Besides the results from the key science programs proposed by the Dark
Energy Survey team, the data archive will be the rich resource for the
community. The DES Legacy archive and the user community science image
archive will become a major digital sky survey, the most extensive in
the Southern Hemisphere until LSST is built, assuming it goes in the
south. The DES data, with uniform depth and coverage, will be well
suited for fundamental astronomical studies - parallaxes, Hess diagram
analysis, and the like. Even in the era of the LSST, the DES data will
be useful for proper motion studies and other time domain studies. We
realize that the utility of the archive to the community will depend on
its accessibility and the robustness of its calibration; we have
designed a survey strategy to meet the calibration requirements for the
data (see Chapter 3 for requirements and Chapter 4 for the survey plan),
and we have developed a data management and archiving plan with the goal
of rapid community data release and ease of community access (see
Chapter 6).

Some of the topics listed in the previous section would benefit from the
DES archive, but in addition, there are additional projects. Here we
describe in detail two specific examples of doing science using the DES
Legacy archive: cluster strong lensing and wide ranging quasar science

**\-- Strong lensing in galaxy clusters**

The incidence of arc formation due to extreme strong-lensing by galaxy
clusters has long been suggested as a cosmological probe. Extensive
research in the 1990\'s, exploiting primarily the X-ray selected EMSS
cluster sample, demonstrated modest consistency between observations and
theoretical predictions for open low-Ω~M~ cosmologies. Since this work,
which culminated in the analysis of Bartelmann et al. (1998), there has
been a growing realization that the predicated arc statistics are in
rather gross disagreement with the flat, Λ-dominated cosmology indicated
by many other probes.

The formation of arcs is dependent on essentially three things: 1) the
cosmology, 2) the source population, and 3) the properties of the lenses
(clusters). The cosmology will be well determined by the DES itself, and
the source population is now relatively well known with further progress
to be expected from follow-up of surveys such as the Hubble Ultra-Deep
Field. A large sample of clusters with arcs could then be used to
understand the (dark) mass structure of galaxy clusters independent of
the luminous component of the cluster.

How many arcs might we expect to see in the DES? Statistics are scarce,
but the most comparable dataset is the \~90 square degrees of RCS data,
which yielded 7 arcs around 5 clusters. The DES data are in more and
bluer bands (and hence likely to pick up more arcs, because arcs tend to
be blue) and typically about 0.5 mags deeper than the RCS. One can thus
readily expect several factors of two gain in finding arcs in the DES
over the RCS. The DES has similar redshift grasp to the RCS, and is
about 55 times larger in area. It is thus reasonable to expect to find
something like 1000 strong-lensing clusters in the DES! Such a sample
would completely revolutionize our understanding of cluster structure
and could present a very powerful cosmological test in its own right.

Finally, it is worth noting that most of the strong lensing by clusters
does not result in arcs, but multiple images which may not be
morphologically recognizable despite being highly magnified. A
particularly striking case of this is the object cB58 (Yee et al. 1996)
which is a 20th magnitude galaxy at z=2.6. The multi-filter observations
of the DES will allow us to recognize such objects as extreme outliers
in magnitude-redshift space, which are on lines-of-sight behind massive
clusters. Given the area of the DES we expect to find perhaps hundreds
of such objects, which will be a profoundly important sample for
studying the very distant universe and the formation of galaxies.

**\-- QSO catalogs**

Wide, multi-band imaging surveys provide incredible resources for
improving our understanding of the Universe. The Palomar All-Sky survey,
2MASS, and the Sloan Digital Sky Survey enabled advances in optical
astronomy, as radio, X-ray, and gamma-ray surveys have done in other
wavelengths, by taking advantage of unique qualities that large
quantities of data offer: ability to find rare, exotic sources and
reduction of uncertainties in statistical distributions due to reduced
Poisson fluctuations.

Other \"large\" statistical samples of QSOs have been compiled by:

-   SDSS \-- 8,000 square degrees to a limiting magnitude of 20.1 for
    80,000 spectroscopically-confirmed QSOs when SDSS is completed. From
    the imaging SDSS data alone, a catalog of photometrically-identified
    quasars to a limiting magnitude of 21 has 100,000 QSOs in 2099
    square degrees (Richards et al. 2004). The highest-redshift QSOs
    ever discovered (up to z=6.4) are also from the SDSS, with
    spectroscopic follow-up from other instruments.

-   2dFQ \-- 750 square degrees to a limiting magnitude of 21 for 49,425
    spectroscopically-confirmed QSOS.

-   COSMOS \-- 2 square degrees, with 600 QSOs to 24th magnitude and
    multi-wavelength observations

-   COMBO-17 \-- 1/4 square degrees, with 100 QSOs to 24th magnitude,
    but with precision photometric redshifts (good to 0.015 in z) from
    narrow-band filters. (Wolf et al. 2004)

-   GDDS \-- 0.1 square degrees, to 26th magnitude (but with extensive
    coverage in many wavelengths)

In comparison, the DES will carry out a quasar survey \-- during the
course of normal operations \-- covering 5,000 square degrees, to 24^th^
magnitude, including 1.6 million photometrically-identified QSOs.

Clearly, this unprecedented data sample opens up new vistas:

1.  The highest redshift quasars: covering an area comparable to the
    SDSS, but 4 to 5 magnitudes fainter satisfies the criteria of Fan et
    al. (2004 ASP Conference Series v311): \"A deep quasar survey at z
    \~ 6 is needed to understand the complete picture of high-redshift
    quasar evolution and its relation to reionization history.\" The
    SDSS has tentatively found evidence of reionization at z \~ 6
    (Becker et al. 2001) and the DES should image many more
    high-redshift quasar candidates than the SDSS, testing this
    reionization claim and potentially mapping the surface of
    reionization, and probing coevolution of AGN, black holes, and QSOs.

2.  The catalog of quasar candidates identified in the 4-filter
    photometry will have photometric redshifts determined to \~0.15, and
    a probability vs. redshift distribution to quantify ambiguities.
    Cross-correlation of this catalog with X-ray and radio surveys, and
    spectroscopic follow-up with multi-fiber spectrographs, are fertile
    research programs. The large numbers allow us to study the evolution
    of the luminosity function with redshift and absolute magnitude.

3.  We estimate the number of quasars in the DES by assuming a faint-end
    slope of the QSO luminosity function of around 0.3. However, the
    distribution of quasars is itself rather uncertain, especially at
    fainter magnitudes and higher redshift. At around g \~ 21 the
    redshift distribution flattens; the DES could unambiguously
    determine whether this is due to survey incompleteness or intrinsic
    to the population of faint quasars.

4.  The clustering of quasars alone provides a wealth of cosmological
    information (see, e.g., Outram et al. 2003). Relationships between
    the clustering of quasars along and perpendicular to the
    line-of-sight provide a strong constraint on the Dark Energy (the
    Alcock-Paczynski effect), while the relative angular correlation of
    the quasar and galaxy populations can provide a constraint on the
    matter density via the skewness statistic, without the need to
    measure accurate population redshifts (Menard, Bartelmann, & Mellier
    2003). While many of these tests may be augmented with spectroscopic
    redshifts, photometric redshifts often provide sufficient
    information, especially in conjunction with ancillary data in other
    passbands (such as might be obtained in conjunction with VISTA).

5.  Lensed quasars offer a unique view of the distant Universe. Knowing
    the number of faint quasars is critical to understanding the effects
    of lensing magnification bias. Assuming typical cosmological models,
    the DES should identify around 50 strongly lensed QSO pairs (as well
    as quadruples, sextuplets, etc.) with separations less than 10
    arcseconds (see, e.g., figures 7-15 of Lopes & Miller 2004). The
    actual number of lenses found can by itself constrain cosmological
    models, subject to uncertainties in halo modeling, in addition to
    the constraints on the Hubble parameter that arise from measuring
    image delays between lensed pairs.

6.  The general DES dataset also provides important information on the
    variability of quasars, via the repeated observations over the
    course of the survey used to build up the entire 5000 square
    degrees. The smaller, supernova-focused area, however, would prove
    even more useful in this regard due to the large number of epochs
    sampled over a number of years to faint magnitude limits.
    Variability is proving to be a powerful method for selecting
    quasars, at a range of redshifts (see, e.g., Rengstorf et al. 2004).
    The SN component of the DES should provide highly-sampled light
    curves (as short as 1 day in the observed frame) for around 20,000
    quasars. This information can be used to constrain the mean lifetime
    of quasars (e.g., Martini & Schneider 2003) as well as the power
    spectrum of accretion that constrain models of accretion disk
    instabilities (Vanden Berk et al. 2004).

In addition to the strides that will be made with this QSO sample, we
expect that the astronomical community could request that imaging be
taken with narrow-band filters.

**9.4 Community Workshop**

The limited sample of science cases listed above is based on the
scientific range of interests of the writers of this proposal. The US
astronomical community, however, can tell us much better what is the
true range of science that the DECam instrument can support. As part of
the Dark Energy Survey proposal, we will call for a workshop to be held
for prospective users of DECam and the DES Legacy archive. This workshop
will be held in 2004 or in early 2005, preferably near the Chicago area
so that most of the DES team can attend. It would be preferable to hold
the workshop before the end of the year 2004.

The goals of the workshop are twofold. We want to hear from the
community about their ideas for the science that can be done with this
instrument. In addition, the DECam project and NOAO need to anticipate
instrumental functions and data products not covered in this proposal.
For instance, what should the form of the archive be? What data products
are crucial to include in the archive? What are the most important
filters that need to be acquired? What should be the role of large
surveys versus smaller pointed surveys? We imagine that users will
suggest novel ways to use the instrument in a manner we have not
anticipated. Can these novel techniques be incorporated into the user
program as run by NOAO without impacting the cost, operation, and
science of the Dark Energy Survey? What will be the preferred community
access to the telescope and instrument during the time of year the DES
is running?

We can expect the unexpected from a community of astronomers presented
with a world class instrument on a mature telescope.

#### References {#references .unnumbered}

#### Bartelmann, M., et al. 1998, A&A, 330, 1 {#bartelmann-m.-et-al.-1998-aa-330-1 .unnumbered}

Becker, R.H., et al. 2001, AJ, 122, 2850

> Lopes, A.M., & Miller, L. 2004, MNRAS, 348, 519
>
> Martini, P., & Schneider, D.P. 2003, ApJ, 597, 109
>
> Menard, B., Bartelmann, M., & Mellier, Y. 2003, A&A, 409, 411
>
> Outram, P.J., et al. 2003, MNRAS, 342, 483
>
> Rengstorf, A.W., et al. 2004, ApJ, 606, 741
>
> Richards, G., et al. 2004, in preparation
>
> Vanden Berk, D.E., et al. 2004, ApJ, 601, 692
>
> Wolf, C., et al. 2004, A&A, 421, 913
>
> Yee, H.K.C., et al. 1996, AJ, 111, 1783

**10. Conclusions**

We propose to make the most precise measurement of dark energy through
four independent, complementary measurements of: (i) the redshift
distribution and spatial clustering of galaxy clusters using both the
SZE (in cooperation with the South-Pole Telescope project) and optical
methods; (ii) weak gravitational lensing of distant galaxies; (iii) the
evolution of the spatial distribution of galaxies; and (iv) the
distances to type Ia supernovae. Our program will yield a determination
of w, the equation of state parameter of the dark energy, to a precision
of 5%. While our focus is on dark energy and dark matter, the survey
data will also be valuable to the community for a broad range of other
science programs.

Our program is the next logical step in the precise determination of the
fundamental parameters of cosmology. The giant leaps in precision of the
next generation experiments require correspondingly long lead times. In
contrast, the Dark Energy Survey can be completed within a decade, and
it fits nicely between the current experiments and the more ambitious
programs that will be reaching the observation stage in the next decade.
Active members of our collaboration are participants in these programs,
and wish to insure their success by taking this step.

We have assembled a strong, experienced team to reach our scientific and
technical goals. We have come a long way since our first collaboration
meeting at Fermilab in early December. At that time our collaboration
had fewer participating institutions and there were gaps in our
technical capabilities that would have hampered our ability to mount
this program. Our new collaborators bring technical capabilities that
more than fill those gaps and their participation will enrich our
scientific program. However we are open to adding institutions that can
reduce the cost of the Dark Energy Survey to the U.S. sponsors, The
Department of Energy and the National Science Foundation. By today's
standards for dark energy explorations this project requires a modest
cash outlay. On the basis of our proposed funding profile, we plan to
begin regular observations in the summer of 2009 with both the fully
commissioned instrument and the validated data processing and archiving
systems. We request 30% of the observing time on the Blanco between the
summer of 2009 and the summer of 2014, concentrated during the period
from September to February when the area of sky we are interested is
visible, in exchange for creating the instrument and data management
systems.

We realize that this is a significant request, but if we are to succeed
we must take significant steps. The science opportunities of the Dark
Energy Survey are important, timely and directly relevant to
astrophysics, cosmology, and particle physics. Our collaboration has the
capabilities needed to successfully execute the project.

[^1]: The exact statement is more complicated. The transition between
    the linear regime and the square root regime is determined by the
    telescope aperture, instrument throughput, point spread function,
    and the sky background level.

[^2]: Area is more important than depth, given that one is starting with
    a 4m telescope and a high efficiency instrument.

[^3]: This is the Honeycomb Conjecture proved by Hales in 1999 after a
    mathematical history of 2500 years.

[^4]: The color scheme seems to be: N tilings is red, N-1 tilings is
    blue, N-2 tilings is yellow, and if there is only one tile, it is
    pink.

[^5]: The number of tilings also directly affects the data volume, as
    each tiling when reduced takes roughly 2 Terabytes of disk space.

[^6]: The algorithm improvements are all based on the idea that if there
    are three or more images, it is possible to vote on the truth on a
    pixel by pixel basis, whereas if there is one or two images, one
    must examine surrounding pixels.

[^7]: The moon actually does not enter the SPT survey area: the moon
    remains within 6 degrees of the ecliptic plane, and the ecliptic
    plane is at its closest 67 degrees away from the South Galactic
    Pole. We can image in z-band in the SPT area for any moon position.

[^8]: The actual spatial pattern is constrained by 1) constant airmass,
    2) select low RA first, then low Dec, and 3) preferentially stay
    inside the current sector, a 15 by 15 degree box. The latter two
    constraints are needed to work on the parts of the sky best visible
    early in the season rather than late in the season.

[^9]: This was unsurprising because the exposure time was chosen to
    obtain 1 tiling per year, and we adjust it accordingly.

[^10]: The difference between relative and absolute calibrations is
    developed in section 4.2.1.

[^11]: Our survey area has 5000 sq-degrees, the DECam has a tiling area
    of 3.0 sq-degrees, and there are thus 1667 tiles in the survey area.
    When we lay down a hexagonal grid on the celestial sphere in the
    simulation, we find we need 1775 tiles. We ascribe the difference to
    a declination dependant tiling area. The mapping of a hexagonal grid
    on the sphere is an interesting problem, one that has been explored
    by the climate modeling community (Randall et al. 2003) , and we are
    examining their solutions while continuing our own exploration of
    the topic.

[^12]: It works well, assuming that the problems in October can be
    resolved by working the hex selection algorithm. This is likely as
    the current algorithm weights equally objects in the east and in the
    west, whereas hexes in the west are more valuable, as they sink with
    season as well as with the passing hours.

[^13]: ^^ this estimate assumes 8 hours per week for the Project Member
    and 2 hours a week for each additional developer. The number of
    developers, of course, depends on the cost of the estimated cost of
    the other packages. We assumed two additional programmers and a four
    year development period.