A Proposal for

Fermilab

To Support the

Dark Energy Survey

Design and Development Phase

Submitted by

Fermilab

J. Annis, S. Dodelson, B. Flaugher, J. Frieman, S. Kent, H. Lin, L. Hui,
J. Peoples, A. Stebbins, C. Stoughton, D. Tucker, W. Wester

University of Illinois at Urbana-Champaign

R. Brunner, J. Mohr, R. Plante, J. Thaler

Cerro Tololo Inter-American Observatory

C. Smith, N. Suntzeff

University of Chicago

J. Carlstrom, J. Frieman, W. Hu, E. Sheldon, R. Wechsler

Lawrence Berkeley National Laboratory

G. Aldering\*, N. Roe\*

\*Contacts for the LBNL Cosmology Group and Microsystems Lab

Proposal Contact Persons

Jim Annis Brenna Flaugher

annis@fnal.gov brenna@fnal.gov

MS 127, Experimental Astrophysics MS 310, Silicon Detector Facility

Fermilab Fermilab

PO Box 500 PO Box 500

Batavia, IL 60510 Batavia, IL 60510

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

1.  **Executive Summary 1**

2.  **The Science Program of the Dark Energy Survey 4**

3.  **Survey Strategy 29**

4.  **The Dark Energy Survey Instrument 43**

5.  **Data Management 68**

6.  **Project Management Structure and Near Term Goals 75**

7.  **The Relationship of the Dark Energy Survey to**

**Other Astrophysics Projects 80**

8.  **Conclusions 82**

Proposal to Fermilab for the Dark Energy Camera

**1.** **Executive Summary**

The National Optical Astronomy Observatory (NOAO) has issued an
Announcement of Opportunity (AO) for a partnership to develop a major
new instrument for the Blanco 4-meter telescope at the Cerro Tololo
Inter-American Observatory (CTIO) in Chile. In particular, they
encourage construction of an instrument that could exploit either the
wide-field capability of the prime focus or the Ritchey-Chrétien focus.
We, a group of astrophysicists and physicists from Fermilab, the
University of Illinois, the University of Chicago, the Lawrence Berkley
National Laboratory (LBNL), and CTIO wish to respond to that AO by
submitting a proposal to build an integrated wide-field imaging camera
and wide-field corrector for the prime focus of the Blanco 4-meter
telescope. Our proposed instrument will have an effective field of view
of three square degrees and its focal plane will contain roughly 500
million pixels. These features will give it greater surveying power than
any optical or near-infrared camera currently in existence. Our
instrument will provide i and z throughputs that are more than a factor
of ten greater than those of Mosaic I and II, the existing wide-field
cameras used on the Blanco and Kitt Peak National Observatory (KPNO)
Mayall 4-meter telescopes. In addition to the proposed instrument, which
will include the CCD camera, corrector, and prime focus cage, our
collaboration proposes to develop the processing and archiving software
for the camera data and to provide the facilities and staff to process,
archive, and distribute the processed data from our survey, the Dark
Energy Survey. The Dark Energy Survey data and catalogs will be archived
and distributed in partnership with NOAO.

Should NOAO select our proposal for the new instrument, we expect that
NOAO will award our collaboration up to one-third of the observing time
on the Blanco telescope over a five-year period in exchange for
delivering the instrument and the software systems. Prior to delivery,,
CTIO plans to upgrade the Blanco telescope controls and infrastructure
to efficiently accommodate the new instrument. Our collaboration plans
to use this awarded time to carry out a survey in four optical passbands
(g, r, i and z) over an area of approximately 5000 square degrees. The
Dark Energy Survey will provide a powerful probe of the Dark Energy
using four complementary measurements of: (i) the abundance and
clustering evolution of galaxy clusters, (ii) weak gravitational lensing
on large scales, (iii) the evolution of the spatial distribution of
galaxies, and (iv) the luminosity distance to type Ia supernova. We
select our survey area in the Southern Galactic Cap so that most of the
survey area, 4000 square degrees, will be visible from the South Pole;
this will allow us to combine our observations with the
Sunyaev-Zel'dovich effect survey planned with the South Pole Telescope
currently under construction.

The Dark Energy Survey will catalog and provide photometric redshifts
for roughly 300 million galaxies out to a redshift beyond one, nearly
four times the number of galaxies in and substantially deeper than the
Sloan Digital Sky Survey (SDSS), the largest survey to date. Like the
SDSS, which has already had a deep impact on science, we expect that our
proposed survey will yield rich scientific data and discoveries in a
very wide range of topics of interest to astrophysicists, cosmologists,
and particle physicists. The SDSS is a very successful international,
multi-agency, public-private partnership, and we believe that we can
achieve a similar degree of success with the Dark Energy Survey.

The Fermilab group proposes to take the responsibility for designing and
building the camera, the prime focus cage, and the mechanical and
electrical interfaces to the Blanco telescope. The Fermilab group also
proposes to design and build the optical corrector in partnership with
CTIO. It also proposes to use the skills and resources of the Silicon
Detector Facility, because those skills and resources are exceptionally
well matched to building a large CCD camera. The LBNL Cosmology Group
and Microsystems Group propose to work in collaboration with the
Fermilab group to develop and package the CCDs and to design, build, and
integrate the front-end electronics chips with the CCDs. The University
of Illinois High Energy Physics group proposes to design and build the
data acquisition system. These groups, the Camera Team, propose to
integrate the camera, the data acquisition system, and the related
mechanical, electrical, and communications interfaces with the Blanco
into a fully tested and functioning unit at Fermilab prior to shipping
the instrument to Cerro Tololo. The Collaboration, with support from the
CTIO staff, will mount the instrument on the Blanco prime focus,
commission it, and then begin the survey. The Camera Team proposes that
their efforts be augmented by the PPD Electrical Engineering Department
and Mechanical Support Department.

The University of Illinois Astronomy group, which will be supported by
the National Center for Supercomputing Applications (NCSA), proposes to
take the leadership of data management and archiving. The Collaboration
plans to archive and distribute the data to the scientific community in
partnership with NOAO. The University of Chicago group plans to
contribute to the science, hardware and software systems, and their
major contribution will be to the science software. All participants
expect to support the observations and participate in quality assurance
of the data and the science analyses. The Collaboration requests some
modest support from the Fermilab Computing Division in the areas of data
acquisition and data management so that we may take advantage of the
experience that the Division gained from building and maintaining the
SDSS data acquisition system and from operating the SDSS data processing
and data archiving center.

In this proposal, we request that the Director approve the Fermilab
participation in the preparation of the proposal that the Dark Energy
Collaboration wishes to submit to NOAO by the August 15 proposal
deadline. The Fermilab group also requests technical support from the
Laboratory in order to produce a preliminary design of the camera and
corrector in sufficient detail to enable the Collaboration to
demonstrate that the technical performance of the proposed instrument
will meet our scientific goals. The preliminary design will include a
cost and schedule for the construction and commissioning phases of the
Camera, and the Camera Team expects to complete this in May. The
Collaboration will augment this support to develop a full cost estimate
and schedule for the entire project that they plan to complete in July.

The Camera Team has developed preliminary estimates of the materials and
services costs and the labor FTEs for the Dark Energy Camera, and these
are included in this proposal. This work has already allowed them to
determine that the development and testing of the CCDs and the front-end
electronics are the most time critical elements in the schedule. They
request sufficient support to initiate a development program that
continues through the end of this calendar year on the assumption that
we will emerge victorious from the competition. In the event that we are
successful, 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 Fermilab group will submit a revised
proposal to Fermilab for the support they need to carry out their role
in the Collaboration. These proposals will include a plan to obtain the
funds to build the instrument and carry out the Dark Energy Survey.

The proposal is organized in chapters as follows: Chapters 2 and 3
describe our Science Goals, the requirements that must be met to achieve
them, and the survey strategy that we plan to use to reach them. Chapter
4 describes the Dark Energy Instrument that will make it possible to
achieve our goals. Chapter 5 describes our first formulation of the Data
Management Plan. Chapter 6 describes the project management structure
for the design phase of the project and the relevant prior experience in
instrumentation and large surveys. Chapter 7 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 8 summarizes the support that the Fermilab Dark Energy
team is requesting in this proposal.

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

What is the nature of the Dark Energy? The National Research Council
Report, Connecting Quarks With the Cosmos (2003), identified this as one
of the most profound questions about the Universe that are ripe for
critical progress in this decade. The Report noted that 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 starting later in
this decade and thereby achieve a substantial advance in dark energy
precision. In this chapter, we describe the science goals and drivers of
the Dark Energy Camera and Survey design.

The Dark Energy Survey data will include accurate fluxes, colors, and
shapes of about 300 million galaxies over an area of 5000 square
degrees. The fluxes and 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 at the South Pole
Telescope (SPT); we will obtain photometric redshifts for the vast
majority of SPT clusters, necessary for exploiting the SZE cluster
abundance and power spectrum as precision cosmological probes. In
particular, we will optically identify and measure accurate photometric
redshifts of *all* clusters in the survey area to a redshift z \~ 1.0
and of a fraction of the clusters extending to z \~ 1.5. We will also
measure the shapes of distant galaxies to infer the shear caused by weak
gravitational lensing. The weak lensing measurements will calibrate the
masses for optically selected 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 will also enable high-S/N measurement 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.
Finally, through repeat scanning of selected areas of the survey, we
will obtain 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, forecast constraints generally depend
on priors assumed for marginalized parameters. As a result, extreme
caution must be exercised in comparing the projected cosmological
parameter sensitivity of different experiments and methods. More
important than the expected statistical precision shown below is the
fact that the different methods we 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.* Chapters 3 and 4 show that such a survey can be
completed in five years with a new \~500 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 counts, 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 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, ~DE~ 0.7, and that its equation of state parameter *w* \< 0.75
(the exact upper bound depends on priors assumed on other cosmological
parameters). 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 precision of \~5% and the
dark energy density ~DE~ to within 0.01.

a.  

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 the
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 a 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="2.870138888888889in"
height="2.8069444444444445in"}

Figure 2.1-1 Current 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 (Tegmark
et al 2003; see also Spergel et al 2003 for an earlier analysis).

**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; 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 number density and
clustering of massive clusters of galaxies as a function of their mass
and redshift. 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 abundance of clusters and its cosmic
evolution provide a sensitive new 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
detect and provide a census of tens of thousands of clusters over a 4000
square degree region south of declination = 30^o^. Because the SZE
signal from a cluster is a measure of the 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 cluster abundance 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 that plague both lensing (e.g., Dodelson 2003) and optical mass
estimators (e.g., Lin et al 2003) are less problematic (although still
present at some level) 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 statistical
pattern of these distortions---for example, its angular power
spectrum---as well as the cross-correlation between foreground lensing
galaxies and background galaxy shear, is sensitive to the cosmic
expansion history and thus to the dark energy. 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. Third, there is great 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 (Cooray et al 2001, Hu & Haiman
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 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.

These four techniques have very different sources of systematic error
from each other. 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. 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 surveys to redshifts z\~1
can measure 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 comoving
volume per unit redshift and solid angle, *d^2^V/dzdΩ* times 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
the 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.

-   **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 *~DE~* = 0.7*, w =* 1, and power spectrum amplitude *~8~* =
> 0.9. A particular realization of the model appears with red points and
> error bars. The green model 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=Δ/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 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="2.9743055555555555in"
height="2.7930555555555556in"}![](media/image6.png){width="2.9659722222222222in"
height="2.7930555555555556in"}

> Figure 2.3-2 Forecasts of the constraints on the dark energy equation
> of state parameter *w*, the dark energy density parameter ~E,~ and the
> matter density parameter ~m~ for the SPT+DES galaxy cluster survey
> (blue). For comparison, forecasts for SNAP supernovae (green;
> Perlmutter & Schmidt 2003), current constraints from WMAPext (black;
> 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 constraints arise from the cluster power spectrum,
> the cluster redshift distribution (assumed distributed uniformly out
> to z =1), and 100 cluster mass measurements each accurate at the 30%
> level (1σ). Note that only the SNAP constraint includes an 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. 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 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 cluster dynamical 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 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, it has been
suggested (Battye and Weller 2003) that the SPT+DES cluster abundance,
in conjunction with external determination of the mass power spectrum
normalization, can provide constraints on the evolution of *w* that
complement those that will come from SNAP.

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

The classical method of identifying clusters is by searching 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 provide estimates of the cluster abundance and
cluster masses that are independent of those from the SZE.

![](media/image7.png){width="2.0743055555555556in"
height="2.5659722222222223in"}

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

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 \~0.30.5 (Annis
et al 1999, Sheldon et al 2001, Bahcall et al 2003; see Fig.2.3-5).
Moreover, 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.

![](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 et al 2001). ![](media/image9.png){width="5.993055555555555in"
height="3.5069444444444446in"}

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 (logarithmic
flux). 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, 3 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~*.

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 a 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.

> ![](media/image10.png){width="4.75in"
> height="3.7979166666666666in"}Figure 2.3-6 A realization of the
> 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 our proposed 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. While 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 fully 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), and denote the
two-dimensional angular galaxy and shear fields, and and respectively
denote the three-dimensional galaxy (*g*) and mass (*m*) density
fluctuation fields at redshift *z*. The weight functions *W* 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 matter 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. 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 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; 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, *^2^(~i~)* is
the variance in a single component of the (two-component) shear, and
*n~A~* is the 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. Following the Appendix (Section 2.8), we use a
fiducial effective source galaxy density n per square arcminute, and
median source redshift 0.7. We note that n here is smaller than the
actual galaxy source density in the Survey, because it includes
weighting due to measurement error, point-spread-function (PSF)
dilution, and shear polarizability. In comparison to the CFHT Legacy
Survey, the signal to noise on the shear variance for the Dark Energy
Survey should be larger by a factor of three

![](media/image13.jpeg){width="3.0in" height="2.879861111111111in"}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.
Although the shear-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. 2.4-1 shows the expected constraints from the Dark
Energy Survey, splitting the source galaxies into several photometric
redshift bins. 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*-* error on *w* of 6%.
These numbers will decrease with improved priors from Planck.

> Figure 2.4-1 68% CL constraints on *w* and . 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.

This statistical accuracy, 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 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 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. 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. 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 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. 2.4-2 (from Joffre et al.
1999).

![](media/image14.png){width="3.7916666666666665in" height="3.375in"}

Figure 2.4-2 Reconstructed projected mass map for the *z* =0.05 cluster
Abell 3266 superposed on a *R*-band image 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.

**2.5 Galaxy Angular Clustering**

The Dark Energy Survey (DES) will deliver a sample of over 300 million
galaxies extending well 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 between
the galaxy and dark matter power spectra can be described by a
redshift-dependent bias factor, *b(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

![](media/image15.wmf){width="2.3194444444444446in"
height="0.2777777777777778in"}, (2.5:1)

where the initial dark matter power spectrum from the early Universe
![](media/image16.wmf){width="0.34652777777777777in"
height="0.2222222222222222in"},
![](media/image17.wmf){width="0.3333333333333333in"
height="0.2222222222222222in"} is the scale-dependent transfer function
for dark matter
perturbations,![](media/image18.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.

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/image19.wmf){width="2.236111111111111in"
height="0.4027777777777778in"}, (2.5:2)

where ![](media/image20.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). With a large sample of galaxies extending over a
broad range in redshift, it is possible to solve for the bias within
each redshift bin while simultaneously constraining the density and
equation of state of the dark energy.

Detailed studies of how well we will be able to constrain the dark
energy using the evolution of the galaxy angular power spectrum are
still in progress. A preliminary estimate using the halo model,
restricting information to angular multipoles *l* less than 300 to avoid
issues of scale-dependent bias, and including CMB priors on the power
spectrum shape suggests that *w* can be determined with a 1- statistical
uncertainty of about 13% with this method.

**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
counts.

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 one third 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 16 Dark Energy Camera fields or roughly 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 4. 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 left panel shows
the SN results combined with the existing large-scale structure results
of the 2dF survey. The right panel shows the combination of our SN
results with the SPT+DES cluster observations, yielding a combined
constraint of roughly ±0.02 on the value of *w*. These constraints are
significantly tighter than those hoped for from the set of SN surveys
currently underway.

These simulations have many assumptions folded in, most based on the
experiences of past and current SN surveys, some of which may not be
directly applicable to the DEC 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 DEC 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 DEC SN survey, such as the SDSS, 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 diameter telescopes worldwide,
and 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 a detailed analysis
of a sample of SNe with such a combined analysis.

![](media/image22.png){width="3.002083333333333in"
height="2.209722222222222in"}

> Figure 2.6-1. Projected constraints on ~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 (~M~ = 0.278 ± 0.042)
> (left) and joint constraints from SNe and the SPT +DES cluster survey
> (right). Contours represent 1, 2, and 3 confidence levels. The curves
> at right represent the constraints on *w* after marginalization over
> ~M~.

It is important to note that the DEC 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
DEC 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 DEC 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 DEC 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

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 more inexpensively 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 ~z~ =0.03 out to redshifts z 0.6. However, at higher redshifts
we do not have such large samples of red galaxies available, so we need
to rely on Monte Carlo simulations to assess the quality of cluster
galaxy photometric redshifts for the Dark Energy Survey.

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 Monte Carlo simulations, we adopt two different galaxy spectral
energy distribution (SED) models to simulate the colors of a red cluster
galaxy as a function of redshift: (1) an empirical non-evolving
elliptical galaxy SED taken from the sample of Coleman, Wu, & Weedman
(CWW; Coleman et al. 1980); and (2) a passively evolving elliptical
galaxy model taken 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, while the CWW model will
provide an alternative SED for comparison purposes. Separate sets of
Monte Carlo simulations are generated with galaxy absolute luminosities
fixed at 0.5L\*, L\*, 2L\*, or 5L\*, where L\* is the characteristic
galaxy absolute luminosity (with 0.5L\* being our faint luminosity limit
of interest). 5000 galaxies are generated per simulation, distributed
uniformly over redshifts 0\<*z*\<1. A flat cosmology, with and , is used
to calculate galaxy apparent magnitudes as a function of redshift, and
the models are normalized so that a 0.5L\* galaxy at redshift one has a
*z*-band magnitude of 23. The 10 galaxy magnitude limits for the Dark
Energy Survey are *griz* = 24.6, 24.1, 24.0, 23.6. Expected galaxy
photometric errors are calculated by appropriately scaling the *S*/*N*
relative to the 10 magnitude limit, and by adding in quadrature a 2%
error floor due to photometric calibration uncertainty. Photometric
redshifts are determined using a polynomial-fitting technique (Connolly
et al. 1995): we adopt a photometric redshift relation of the form
redshift = *f(g,r,i,z,g^2^,gr,r^2^,...)*, where *f* is a polynomial
containing terms up to quadratic order in the *griz* magnitudes.

![](media/image25.png){width="3.875in" height="3.875in"}

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

Representative simulation results for 0.5L\* and 2L\* galaxies are shown
in Figure 2.7-3, which plots the difference between photometric redshift
and true input redshift as a function of the true redshift. We find good
photometric redshift results for 0\<*z*\<1, with a 1 scatter ~z~\<0.03
per galaxy and only small systematic biases of size \<0.01 in redshift.
Note that we can do even better for a whole galaxy cluster, since we can
average the photometric redshifts for, say, *N* individual cluster
members and improve the photo-z estimate by the expected factor of .

One can see from Fig. 2.7-3 that there are small differences in the
results for the CWW vs. Pegase-2 models, a consequence of the detailed
differences in the color vs. redshift relations for the two galaxy
models. In practice, for the Dark Energy Survey 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.

### *Photometric Redshifts for the General Galaxy Population*

We would also like to obtain photometric redshifts for the general
galaxy population, beyond red cluster galaxies, in particular to
facilitate dark energy constraints using weak lensing, galaxy
clustering, and supernova measurements. Such photo-z's are necessarily
somewhat less accurate than those possible for cluster galaxies, as we
must consider a much broader distribution of galaxy SEDs. Nonetheless,
good photometric redshift measurements out to *z* 1 are still readily
achieved for the general galaxy population.

![](media/image26.png){width="5.124305555555556in"
height="5.124305555555556in"}

> Figure 2.7-3 Photometric redshift results for the 0.5L\* and 2L\*
> cluster galaxy
>
> Monte Carlo simulations. The red lines show the median difference
> between
>
> photometric and input redshift, the blue lines show the 1 scatter (68%
> limits),
>
> and the green dashed lines are set at *z* = 0.03.

We demonstrate this using 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 from
the compilations of Wirth et al. (2004) and Cowie et al. (2004). The
*VRIz* photometry serves as a best-effort approximation to *griz*, as we
are not aware of a sample with *griz* photometry of sufficient depth for
this purpose. We add noise to the original *VRIz* photometry in order to
match the Dark Energy Survey depths. We again derive photo-z's using
polynomial fitting, including terms up to quadratic order in the *VRIz*
magnitudes, and the results are shown in Figure 2.7-4. We find a
photometric redshift scatter ~z~0.05 at *i* =22, increasing to ~z~0.1 at
*i* =24, the 10 galaxy limit for the Dark Energy Survey. The photo-z
scatter increases at fainter magnitudes, as expected, and also increases
with bluer galaxy color, a consequence of the weaker break features seen
in the spectra of blue star-forming galaxies. Note that the photo-z
trends vs. spectroscopic redshift 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. In general, it will be important to understand any such
biases 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 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 Dark Energy
Camera, ![](media/image27.png){width="3.0in" height="3.0in"}enabling us
to derive detailed photometric redshift calibrations for the Dark Energy
Survey.

> Figure 2.7-4 Photometric vs. spectroscopic redshifts for the general
> galaxy population
>
> in the GOODS/HDF-N field. Noise has been added to the original *VRIz*
> magnitudes in
>
> order to match the depths that will be reached by the Dark Energy
> Survey.

## Appendix: Shear Sensitivity and Source Density for the Dark Energy Survey

For galaxy shape measurements, the deep *i* and *z* band exposures,
which should also have the best seeing, will be the most useful. To be
conservative, we consider only the 900 second *i* band exposures (in
practice, the noise in shape estimates can be reduced by combining
measurements in different bands). To estimate the shear sensitivity of
the Survey, we studied a 900 sec CFH12K *I* band exposure taken in
median seeing of 0.63\'\' (the CFHT mirror aperture is close to that of
the Blanco 4m). 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 estimating the shear, the ellipticity measurement of each source
galaxy is relatively weighted by the inverse noise, which has
contributions from shape noise (the intrinsic variance of galaxy shapes)
and shape measurement error, an estimate of which is returned by the
adaptive moments code for each object. 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. 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 10 magnitude limit for galaxies.

In the absence of measurement error and PSF dilution, the variance of
the single component ellipticity for sources covering area *A* is given
by . Including measurement error, PSF dilution, and the galaxy shear
polarizability, we can write the rms as , where is the source galaxy
density including noise weighting. 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. The effective
source density for the DES *i* band images is shown in the bottom panel
of Fig.2.8-1 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.2.8-1 shows that for 0.9\'\' seeing, which should
be typical for the DES, we expect an effective source galaxy density of
about 10 arcmin. We have used this estimate in the analysis of the Dark
Energy sensitivity above. The upper panel shows the shear sensitivity
per component, which is just evaluated at degrees.

The survey depth also determines the redshift distribution of the source
galaxies. In the estimates above, we used a 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 10 detection limit, and (b) fainter galaxies contribute less
weight. These correction effects go in opposite directions, and we
expect the adopted median source redshift above to be reasonably
accurate.

> Figure 2.8-1 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.

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**3. Survey Strategy**

**3.1 Introduction**

The goals of our survey require a large area map of 5000 square degrees
of sky to i=24 in 4 bandpasses, with photometric calibration sufficient
for precise photometric redshifts for all galaxies, implying photometric
calibration accuracy of around 2%. This chapter describes the Dark
Energy Survey and provides a proposal for the imaging strategy and
photometric calibration. We intend to continue to optimize these
strategies.

We first summarize the science goals described in Chapter 2, and follow
that with a section laying out the decisions and calculations that lead
to many of the technical specifications. We then describe tilings as a
way to think about collecting the survey data, and use tilings in a
photometric calibration strategy designed to achieve the calibration
accuracies the survey needs. At the end of the chapter, we give the
expanded technical specifications that flow from the consideration of
the science program as a survey project.

**3.2 Science Goals**

The dark energy probes we outline in Chapter 2 constrain dark energy
using a four-fold approach of cluster abundances, weak lensing shear,
galaxy power spectrum, and supernovae. These science goals may be
summarized as follows:

-   5000 square degrees in the South Galactic Cap, overlapping SPT and
    redshift survey areas.

-   Photometric redshifts to z=1.0 and ½ L\* with δz \<= 0.05, using
    SDSS g, r, i, z. This implies a 2% photometric calibration.

-   PSF sufficient for weak lensing, FWHM \<= 0.9".

We will develop these science goals into technical requirements in the
following sections.

**3.3 Science Goals to Survey Specifications**

***3.3.1 Survey Footprint***

We plan to image 5000 square degrees in the following areas:

-   The South Galactic Cap: The primary decision that shapes the
    footprint is overlap with the South Pole Telescope survey area, 4000
    square degrees area bounded by Dec ≤ -30° and 20^hr^ ≤ RA ≤ 6^hr^.
    The south celestial pole, overhead of the South Pole, is an area of
    high galactic dust and thus has higher uncertainty on magnitudes and
    colors, and for the SPT is an area of higher foreground. We will
    excise this area from the survey. The primary survey area spans
    --30˚ declination to --75˚ declination, extends from 20hr to 6hr,
    and covers the bulk of the low extinction area available.

-   The 700 square degrees area: We select a region that connects the
    main survey area with the photometric redshift area. This region is
    optimally placed for providing targets and optical follow up for the
    large optical telescopes and radio telescopes in Chile.

-   The equatorial stripe: The 200 square degrees region on the equator
    covers existing redshift surveys from the SDSS and VLT. Data on
    these areas are needed to calibrate the photometric redshifts. We
    will need on the order of 1000 redshifts per 0.1 bin in z to
    calibrate the photometric redshifts, so we must image areas already
    under study in large redshift surveys. The equator between 21h and 6
    hr contains the SDSS Southern Survey, 100,000 redshifts of a variety
    of galaxy and stellar samples, probing higher redshifts, fainter
    magnitudes, and smaller objects than the main SDSS survey. This area
    also contains 8 square degrees of the VIMOS-VLT Survey Wide (down to
    magnitude I~AB~ = 22.5) and 2 square degrees of the VIMOS-VLT Survey
    Deep (down to magnitude I~AB~ = 24.5).

![](media/image28.png){width="3.4972222222222222in"
height="3.4972222222222222in"}

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

2.  ***Limiting Magnitudes***

Our science goals demand that we reach the magnitude of a 0.5L\* galaxy
at z=1.0, where L\* is a luminosity characteristic of the galaxy
population. From Blanton et al (2003) we find that a \* galaxy has an
i-band absolute magnitude (coverted to redshift 0 and evolution
corrected). Using a passively evolving old stellar population model
appropriate for high luminosity cluster galaxies, we find that this
corresponds at redshift one to z band magnitude z=23.3. We will be
requiring 10 observations, as needed by our optical cluster finding and
photometric redshift techniques. Our existing test data show that 10 is
near where the number counts turn over, an indication that it is close
to the completeness limit. We will want to go somewhat deeper, say 0.3
magnitudes, in order to safely construct a flux-limited sample of
galaxies. We therefore require a limiting magnitude in the z band of
z=23.6.

We need precise colors on the cluster galaxies in order to calculate
photometric redshifts. We already know the limiting z magnitude, so we
must calculate the g, r, i magnitudes. The magnitude required is the
magnitude of the galaxy at the redshift where the 4000Å break leaves the
blue filter and enters the redder filter. This happens at redshifts of
0.35,0.65,1.0 for the SDSS filters g, r, i. Using the same passively
evolving model, we find that these redshifts correspond to magnitudes g,
r, i = 22.8, 23.4, 24.0.

Table 3.1

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

  Limiting magnitude (10)       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                21.7      20.7         20.0      18.7
  (mag/sq-arcsec)                                                

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

  Minimum integration times     27        130          900       1600
  (sec)                                                          
  ----------------------------- --------- ------------ --------- ----------

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

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

3.  ***Integration Times***

The integration time is the total of all individual exposures.

We will need to calculate the exposure times to reach a signal to noise
of 10 at the limiting magnitudes. 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.

The Blanco 4m telescope has a light collecting area of 10.0 sq-meter. We
will use the SDSS filters, but our reference design 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) a x5 better QE
in z as we are using high red response full depletion CCDs, (2) a wider
z filter, and (3) an additional overall transmission factor of 0.9,
modeling the losses in transmission due to the extra two 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, and we tabulate it in Table 3.1. The sky
brightness varies in time. 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 max is roughly 2012, we note that
the sky brightnesses in Table 3.1 are from SDSS data taken in the years
near the last solar maximum. Table 3.1 also shows the effect of
observing near a full moon (to be precise, within 45 degrees of a moon
at a phase fuller than half), using data from the SDSS Photometric
Telescope. This line shows that we can image through the i and z filters
in such conditions, so called grey time.

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 4m is 1.0", and use an
aperture of 1.7" diameter, as is appropriate for the measurement of the
colors of faint galaxies, to measure the magnitude.

The result is the exposure times listed in Table 3.1.

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

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

4.  ***Available Time and Weather***

We assume our survey will use 30% of the time available over a five year
period.

We have estimated the number of useful hours available for the survey in
the five year period 2008-2012 via an analysis that includes the
observing impact of weather and the astronomy of the lunar cycle. The
details of this calculation are presented in Wester (2004) and are
summarized below.

### 

> **Astronomy-** We define dark time as the hours between the evening
> and morning 18-degree astronomical twilight when no moon is above the
> horizon. Grey time is defined similarly but allows for the presence of
> the moon and 18- to 12-degree twilight. We assume a survey that
> includes a total of 143 nights (5 lunar cycles) that end in January
> prior to the first night during which there is no dark time because
> the moon is always above the horizon. This 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.
>
> **Weather-** To estimate the impact of weather, we analyze data made
> available by CTIO that 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 have also performed a more sophisticated analysis that maps
> the historical weather statistics in all five-year periods from
> 1975-2003 onto our planned observing time in 2008-2012. In this way,
> we have studied the effects of extreme conditions (such as El Nino
> years) as well as the effect of averaging over a five-year period. We
> find average values of 1749 dark and 2142 grey hours. See Figure 3.2,
> where the average time, both dark and gray, is plotted as a dashed
> line.

We will not have the entire 5 lunations available to us. We multiply the
5 lunation total by 0.75 to model the time allocated to us by NOAO. We
thus obtain 1300 hours of useful dark time and 1600 hours of useful grey
time and an estimate of 2900 hours of photometric time over the survey.

Our calculation of survey time includes only photometric time. There is
another 950 hours available with non-photometric, but not overcast,
conditions. Some data taken during this time will be usable, if of lower
quality.

![](media/image29.png){width="5.420833333333333in" height="3.86875in"}

> Figure 3.2 The total cloudless time over a 5-lunation period ending in
> January at CTIO, over the last 30 years.

***3.3.5 Camera Size***

We start from the observation that a 2.1^o^ field of view corrector is
technically feasible on our timescale, and that the area of an inscribed
hexagon is 2.9 square degrees. We will show that a camera of these
dimensions is sufficient for our science goals.

For the purposes of the following calculation, it is convenient to think
of the data as making up a field as being taken at one time. We define a
field to be all observations taken through all bandpasses on a given
location on the sky. The total exposure time is taken to be 3500 seconds
per field, as given in Table 3.3

The overhead is estimated in two parts, slewing and everything else. The
slew time of the Blanco Telescope is 35 seconds for slews less than 4
degrees, greater than both the predicted CCD readout time (18 seconds in
Chapter 4) and the predicted filter change time (20 seconds in Chapter
4). We assume that the total integration time is broken into 5 or more
shorter exposures of at least 100 seconds (see the photometric
calibration section later in this chapter). We assume that the telescope
is moved after every exposure. We note that the overhead may be reduced
by increasing the individual exposure time for data taken in filters i
and z, and maybe somewhat reduced by taking data in two filters before a
slew. The total number of images over all bandpasses is 35, leading to
35 slews and a total of 1200 seconds of overhead per field attributable
to slewing. We take nightly setup time as 0.5 hour, and we assume 4
observations of standard stars per night that take a total of 0.5 hour.
We then assume that equipment failure over the life of the survey
averages to 0.5 hour per night. Taking this overhead per night of 1.5
hours, dividing by 6 fields per night (the survey average is a 9.5 hour
night and 1.5 hours is taken up), we find 900 seconds of overhead to add
to the slew times. The total overhead is 2100 seconds. The grand total
time spent per field is then 5600 seconds. These numbers are summarized
in Table 3.2.

Table 3.2

+------------------------------+---------------------------------------+
| Filter:                      | g, r, i, z                            |
+------------------------------+---------------------------------------+
| Total integration time:      | 500, 500, 900, 1600 seconds           |
+------------------------------+---------------------------------------+
| Number of exposures:         | 5, 5, 9, 16                           |
+------------------------------+---------------------------------------+
| Total integration per field: | 3500                                  |
+------------------------------+---------------------------------------+
| Pointing overhead:           | 35 seconds slew, 35 images: 1200      |
|                              | seconds.                              |
+------------------------------+---------------------------------------+
| Other overhead per field:    | # 90                                  |
|                              | 0 seconds {#seconds .Section-Heading} |
+------------------------------+---------------------------------------+
| Total per field:             | 5600 seconds                          |
+------------------------------+---------------------------------------+

We have 2900 hours available to us, and we plan to devote 10% of the
time to supernova observations, leaving 2600 hours for the survey.
Observing 5000 deg^2^ over 2600 hours implies an observing rate of 1.9
deg^2^/hr. The time spent on each field is 5600 seconds (1.55 hrs), so
the camera area needed is then

> Area = 1.9 deg^2^/hr \* 1.55 hrs/field = 3.0 deg^2^

Note that a corrector with a 2.1 degree diameter field of view has an
area of 3.5 square degrees. The largest useful pattern for tiling the
sky is a hexagon, which if inscribed in 2.1 degree diameter circle has
an area of 2.9 sq-degrees. The current CCD layout in Chapter 4 has a
tiling area of 3.0 sq-degrees with a 5% non-active area. We see that our
current camera design is sufficient to perform a 5000 deg^2^ in the time
allotted to us.

Note that we only included photometric time in this calculation. At
CTIO, the majority of time during our observing season is photometric,
still we estimate in section 3.3.4 that 950 hours of usable
non-photometric time is available and some of it will be useful for
survey data.

***3.3.6 Seeing***

Both the site seeing and the weather statistics suggest that the best
seeing is Oct. to Jan. and we hope to observe during that time. The FWHM
of a star image scales with wavelength and airmass as:

where *z* is the zenith angle, λ is the wavelength and sec(z) is
approximately the airmass. For the same airmass, 0.9" seeing in r band
corresponds to 0.8" in z band, and seeing of 0.8" become 0.9" when the
airmass changes from 1 to 1.2.

In order to attain this seeing, the camera must be kept in focus and the
telescope tracking the stars. We calculate that in 1 second scale
exposures we will have 10 measurements of stars down to i band
magnitudes i\~18.5. We estimate the stellar density at these magnitudes
to be 0.2 stars/sq-arcmin. We find that we need 25-50 arcmin^2^ of
guider area to have a low likelihood of not finding a star to guide on
over the course of the survey. For focusing, we will use survey length
exposures, but we need \~100 S/N measurements of stars. The magnitude
range of interest is roughly the same as for guiding, so the stellar
density is roughly the same.

3.  **Tiling Strategy**

Tilings are unique, non-overlapping coverings of an area. Hexagons can
tile the plane, whereas circles either overlap or leave gaps. Hexagons
provide the optimal tiling of the plane, a 2d space filling pattern that
has the maximum area for its longest cross section. An actual focal
plane layout of CCDs may only approximate a hexagon; what is then
important is the area of the tile in the tiling.

There are optimal mapping strategies for hexagon maps in the sense of
maximizing unique interlocking overlaps for photometric calibration
purposes. Arbitrarily take the first tiling as the reference tiling. 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 3.3a). 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 3.3b). 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. Diminishing returns enter after 6
tilings, and one may either repeat the sequence or dither.

> Figure 3.3. a) The left panel shows the reference tiling in black, and
> the two 33% overlap tilings in red and blue. b) 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.

There are about 1667 tiles in our 5000 square degrees survey given the
3.0 square degrees camera layout in Chapter 4. We currently estimate
between 25 and 35 tiling of the area.

4.  **Photometric Calibrations**

***3.5.1 Photometry of Large Area Surveys***

We are proposing a large area, multicolor, imaging survey that requires
precision photometry. Traditional photometric reduction which places
observations onto a standard system are inappropriate for extremely
large sets of homogeneous photometric data produced using a single
instrument. It is much more important for these surveys to be internally
consistent than it is for them to be on previously established
photometric systems. For extremely large 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 are the ratio of the
    photon fluxes, independent of position.

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.

***3.5.2 Determining the System Response***

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. Ideally one would
include the primary mirror, but in practice the reflectivity curve of
aluminum is well known and flat vs. wavelength. We plan to measure
response curves that include the effects of the correctors, filters, and
the CCD quantum efficiency. Convolutions of spectral energy
distributions with the system response curves are best done with at
least 100 resolution elements across the bandpass. For our \~150 nm wide
filters, we need about 1.5 nm resolution. Detailed calibration is done
once a season for every CCD. We are investigating several options for
this purpose, including one more optimized for supernova operations.

***3.5.3 The Photometric Calibration***

The equation for photometry is

> *m* = -2.5 log*C* - *a*- *k*(*t*)*X* - *cMX*

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. The aim is to reduce the
complexity of this equation.

**Ignore second order extinction:** Wide area surveys usually obtain
observations at low to moderate airmasses. The SDSS maximum airmass
limit was 1.5, and the median airmass was about 1.3. The values of *c*
are of order 0.03, so the second-order extinction term is of order
0.005. Ignoring it places a photometry calibration error floor somewhat
below 1%.

**Absorb k(t)X into the zeropoint:** The zeropoint *a* changes randomly
night to night, while the extinction coefficient *k*(*t*) changes
randomly 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*)

All-sky photometry as routinely practiced is suboptimal for a pointed
survey that covers a large fraction of the sky multiple times, as it
spends far too much time deriving separate zeropoints and extinction
coefficients. With the large amounts of data taken at a single airmass,
as anticipated for our survey, the equations and photometric calibration
become more straightforward.

***3.5.4 Photometric Calibration Through Mapping Strategies***

Techniques developed for CMB mapping strategies (Wright 1996^2^, Tegmark
1997^3^) can be applied to the problem of developing photometric
calibrations across the survey area. 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.

The maximally overlapping tilings make this a powerful technique, and we
can use them in at least two ways:

-   Absolute photometry, where one uses standard stars to tie hexes
    together.

-   Relative photometry, where one uses the ratios of the fluxes of the
    same stars observed on two different images to tie the photometry
    together.

The first, the absolute photometry strategy, would follow from our
reference design observing plan, where we observe constant airmass
tracks and observe a standard star afterwards. We would then have about
4 standard stars a night, which translates to roughly 5% all sky
photometry for the night. Assigning the calculated zeropoints to each
hex, one uses the multiple observations of the hexes in a least squares
fit, involving all hexes observed, to reduce the scatter in the
zeropoints across the final tiling. Our simulation suggests one gets a
$\sqrt{N}$ reduction of the scatter in the zeropoints, where N is the
number of tilings in a given filter.

The second strategy is even more powerful, because of the precision of
relative photometry. It is clear that one knows the relative flux
between overlapping images much better than one knows the zeropoints of
the images. The numerical precision of relative photometry is much
better than 1%, when one is talking about stars appearing in two
different images. What limits this approach is systematic errors,
primarily flat fielding and scattered light. Both of these are more
likely to be more problematic at the edges of a camera that fills a
focal plane. The overlapping hex tilings allow one to average these out.
The relative photometry strategy produces a map which is very flat, and
in that sense has very good relative magnitudes. The zeropoint itself is
unknown. We would either have to use the absolute photometry strategy in
some fashion to calibrate this map, or to use perhaps 100 stars of known
magnitudes in the map to provide the absolute calibration. The likely
saturation point of the CCDs will be roughly i band magnitude i=17.5,
and the likely point of onset of non-linear behavior in the CCD
amplifiers a factor of 2 lower, at roughly i band magnitude i=18. This
means that we will not be able to use the SDSS standard stars of Tucker,
Smith, and collaborators directly, and will have to explore the options
more thoroughly.

***3.5.6 Quality Assurance: Checking the Photometry***

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'll 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 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.

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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.

3.  **Survey Simulations**

We are developing simulations to help us understand the survey in
operation.

***3.6.1 Observing Strategies***

If one observes at a single airmass, one obtains data with great power
to perform cross checks on zeropoints. We developed a survey simulation
and used it to model a survey consisting entirely of constant airmass
tracks.

The simulation consists of the celestial timings, the survey area, and a
simple weather model. It was used to explore whether one can in fact
observe a large sky area efficiently by following constant airmass
tracks. Applying a simple set of rules for the observing strategy, such
as \"find closest hex at predefined airmass\", with a set of stopping
conditions, such as \"no unobserved hex with that airmass available\",
leads to data taken along paths as shown in Figure 3.4. This observing
strategy leads to stripes of a wide variety of lengths and trajectories
across the region, naturally connecting neighboring tiles on
observations from different nights, and to a low median airmass of 1.2.

![](media/image30.png){width="3.6597222222222223in"
height="1.898611111111111in"}

> Figure 3.4. This shows the paths of the constant airmass observation
> stripes for one tiling. Clearly there will be many interconnections
> across tilings.

***3.6.2 Absolute Calibration Strategies***

We extended the survey simulation we developed above to include a model
for the atmosphere that had, among other effects, a time varying
extinction coefficient on each night. 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 3.5.

![](media/image31.png){width="2.7255905511811025in"
height="1.5161417322834645in"}![](media/image32.png){width="2.7720472440944883in"
height="1.5381889763779528in"}![](media/image33.png){width="1.955511811023622in"
height="0.10984251968503937in"}

> Figure 3.5 This figure shows maps of the photometric calibration
> error. The left panel shows the map resulting after 5 years using the
> absolute calibration strategy. The rms scatter in this map is =0.037,
> starting with photometry good to =0.10. The panel on the right shows
> the map resulting after 3 years using the relative photometry
> approach. The rms scatter in this map is =0.013, starting with
> photometry good to =0.1, and with systematics imbedded at the =0.03
> level. The bar at the bottom shows the lookup table for errors of
> -0.20 to +0.20, a 2 range on the input rms scatter

***3.6.3 Relative calibration strategies***

We further extended the survey simulation to include small and large
scale flat fielding errors and small and large-scale scattered light
errors, all on the scale of 2%. We solved for the relative photometry
map using the coadd technique described above. For one tiling
observation the resulting map is flat to =0.034, for two tilings the map
has =0.018, and a five tiling map has =0.010. An image of the resulting
map is shown in Figure 3.5. There is clearly great promise in this
approach.

**3.7 Resources for Survey Operations**

The Survey Strategy team will operate as a working group with Annis as
lead and Lin as deputy with contributions from Flaugher, Frieman, Mohr,
Tucker, Wester. There will be the need for an algorithmic computing
professional to help with the development and production of the
simulations.

This group will continue the development of the survey simulation. This
has several parts.

-   The observing model will have to be developed with an eye towards
    maximizing efficiency.

-   The photometric calibration strategies will need to be extended,
    especially to incorporate improved models for the atmosphere and the
    instrument.

-   Catalog level simulations will extend these simulations all the way
    to the data stream. We have a start with Wechsler's catalog level
    simulations of the galaxies and clusters we expect out to z=1.2.

-   Image simulations that incorporate more sophisticated models of the
    instrument and of the astrophysics will be valuable. We have a start
    on this with Lin and Sheldon's image simulation. The image
    simulation effort will provide the data for the mock data challenge
    (described in Chapter 5), where we send a large amount of simulated
    survey data through the reduction stream as a test of the system.

We aim to understand and characterize the camera as an astronomical
photometry system. This requires that we develop a model of the camera
containing:

-   CCD characteristics

-   System response

-   Flat fields

-   Scattered and stray light

-   Optics

These effects will be included in the simulations. The information
required for the camera model will come from various sources. The CCD
characteristics measured by the CCD development group. The measurement
of the system response, will require designing and implementing hardware
work with the completed camera. The scattered and stray light analysis,
will be contracted, and the results of their analyses incorporated into
the simulations.

**3.8 Summary**

The dark energy probes outlined in Chapter 2, require a survey whose
science goals may be summarized as follows:

-   5000 square degrees in the South Galactic Cap, overlapping SPT and
    redshift survey areas.

-   Photometric redshifts to z=1.0 and ½ L\* with δz \<= 0.05, using
    SDSS g, r, i, z. This implies a 2% photometric calibration.

-   PSF sufficient for weak lensing, FWHM \<= 0.9".

The analysis of this chapter developed these goals into a survey
specification, which may be summarized as:

1.  5000 square degrees in the South Galactic Cap

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a.  4000 square degrees to overlap the SPT survey area

b.  270 square degrees of overlap with redshift surveys for photo-z
    training samples

c.  700 square degrees optimal for CTIO and Atacama telescopes

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2.  Photometric redshifts to z=1.1 and ½ L\* with δz \<= 0.05.

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a.  SDSS g, r, i, z are sufficient, if z is made 5x more sensitive

b.  g=24.6.1 r= 24.1, i=24.0 z=23.6

c.  photometric calibration to 2%

d.  at least 5 exposures per filter

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3.  PSF and galaxy density sufficient for weak lensing

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a.  Seeing \<= 0.9"

b.  Pixel size to fully resolve PSF

c.  Focus and guiding sufficient to obtain 0.9"

The technical specifications that follow from this analysis:

1.  A survey instrument with sufficient grasp

    a.  optics with a 2.1 degree diameter field of view

    b.  a focal plane array with a tiling pattern size of 3 square
        degrees

    c.  quantum efficiency in the z band of ≥ 50%.

For completeness, we show the limiting magnitudes that result from these
survey specifications. We go deeper than required in g and r in order to
achieve the photometric calibration requirement of having 5 or more
exposures per filter.

Table 3.3

  -------------- -------------- -------------- -------------- --------------
  Magnitude and                                               
  Exposure Times                                              

  Filter         g              r              i              z

  Exposure       500            500            900            1600

  mag (10)       24.6           24.1           24.0           23.6
  -------------- -------------- -------------- -------------- --------------

**References**

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

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

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

**4. The Dark Energy Survey Instrument**

# 4.1 The Reference Design

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 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 develop a reasonably complete
enumeration of the tasks that must be executed in order to build the
Dark Energy Survey Instrument. In Section 4.2, we describe the present
configuration of the Blanco and in sections 4.3-4.8, we give the
reasoning behind 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 preliminary Work
Breakdown Structure (WBS) and identified the critical items that will
require development before production can start. We have made a very
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 section 4.9 along with a brief outline of our R&D
plans for the near term. 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.

**4.2 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 4.1 and a drawing of the
layout of the optical components in the Blanco cage is shown in Figure
4.2. 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 4.1 and 4.2.
Mosaic II consists of eight 2K x 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 microns/arcsec (1
meter/5 degrees). Table 4.1 provides a further enumeration of some of
the essential parameters of the Blanco telescope.

> Figure 4.1 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/image35.png){width="5.977083333333334in"
height="2.9229166666666666in"}

> Figure 4.2 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 4.1 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 microns/arcsec                       |
+----------------------------+-----------------------------------------+
| Seeing FWHM                | 0.8 to 1.1 arcsec                       |
+----------------------------+-----------------------------------------+
| Dome 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      | \~2cm                                   |
| focus                      |                                         |
+----------------------------+-----------------------------------------+
| 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. We also propose to replace the
mechanical, electrical, and communication interfaces between the cage
and telescope. The f/8 secondary mirror will no longer be permanently
attached to the cage. Instead a precision mounting system that will be
permanently attached to the cage will allow the mirror to be reinstalled
during the day to the required alignment tolerances.

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.

**4.3 Technical Specification**

-   
-   

Our survey goals and the survey strategy were presented in Chapters 2
and 3. When combined with the performance parameters of the Blanco
Telescope, the site conditions and infrastructure our science goals
determine the technical specifications of our proposed instrument. A
brief summary of those specifications is as follows:

-   A 2.1 degree field of view corrector, and a focal plane in which
    \>\~80% of the field of view is active.

-   Four filters with appropriate bandpasses to determine the redshifts
    of red galaxies by their colors to a typical precision of 0.02 in
    redshift over a redshift range from 0.1 to 1.

-   CCDs with a QE in the z band of \>\~50% to reach the magnitude
    limits of 24.0 in the i band and 23.6 in the z band.

-   

-   A pixel size that allows sufficient sampling to permit precision
    photometry.

-   Guide and focus systems with interfaces to the Blanco telescope
    drive motors.

-   A system capable of providing sufficient Liquid Nitrogen (LN2) to
    cool the CCDs.

Table 3.2: The median seeing at the Cerro Tololo site.

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

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

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

                                                                        

                                                                        

                                                                        

                                                                        

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

4.4 General Layout of the Reference Design

Table 3.2: The median seeing at the Cerro Tololo site.

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

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

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

                                                                        

                                                                        

                                                                        

                                                                        

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

The Reference Design has six distinct subprojects. These are:

1)  the primary cage and integration with all cryogenic, electrical, and
    mechanical systems

2)  the optical system

3)  the CCDs, CCD packaging and front-end electronics inside the camera
    vessel

4)  the camera vessel and cooling system, including the mechanical
    support for the CCDs

5)  the data acquisition system

These will be discussed in detail in the next sections; this section
gives a brief overview of the layout.

Figure 4.3 shows a solid model of the Reference Design of the prime
focus cage. The optical configuration is based on a design commissioned
by NOAO for a 2.1 deg. field of view corrector. The corrector components
are shown 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.

The camera vacuum vessel contains the CCD focal plane, the liquid
nitrogen cooling system, and all power and signal feed-throughs. We have
elected in the Reference Design to process the CCD analog signals and
convert them to digital signals inside the camera by using the CRIC2
chip that is currently in development at LBNL. If this choice proves
feasible, only minimal space will be required (much less than the 4
crates shown in the figure) for the electronic components that must be
located inside the cage and outside the camera vessel. The Reference
Design makes provisions for four standard VME crates and they are shown
next to the camera in Figure 4.3. To provide more space for the
cryogenic connections and the control and signal cables 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 may
show that this is unnecessary.

![](media/image36.png){width="5.99375in" height="4.014583333333333in"}

> Figure 4.3: 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 (green) and 4 crates (purple) for data
> acquisition electronics (may not be needed).

**4.5 Cage and Integration**

Figure 4.4 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 attaching to the left end of the
camera vessel and the CCD cable connections on the sides of the vessel.

> Figure 4.4. Proposed layout of the prime focus cage with the 2.1
> degree corrector, filter changing mechanism, shutter and camera
> 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 are also in close contact with our NOAO representative (T.
Abbott) for discussions of integration issues and have initiated the
construction of a scale model of the telescope and surrounding service
areas. 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 on the Blanco.

**4.5** **Optical** **System**

***4.5.1*** ***Corrector***

Preliminary designs of the corrector were commissioned by NOAO and are
currently being evaluated. For the two 2.1 deg. FOV designs, the largest
optical elements are \~900 mm in diameter. For our Reference Design we
use the concept study by Prime Optics of Eumundi, Australia. The design
has 4 elements made of fused silica, as shown in Figure 4.5. The details
of the component locations are given in Table 4.2. It is necessary to
use lenses with aspherical surfaces on one face of each element to
achieve the desired level of image correction over the full field of
view. Designs with all-spherical elements exist but require the use of
additional elements.  The current design with aspheric surfaces is
optimized for wavelengths of 0.4 to 0.9 microns with lateral chromatic
aberrations controlled only within the bandpass of each filter. The
desired performance is such that D80 (the diameter enclosing 80% of the
light) be smaller than

0.6 arcsec at all wavelengths and positions; this value corresponds to a
FWHM of order 0.4 arcsec.  This performance is indeed achieved for all
wavelengths at all positions except for the 0.4 micron wavelength, which
grows to a D80 of 0.8 arcsec in an annulus of width 10 mm at the edge of
the field.

![](media/image38.png){width="4.315972222222222in"
height="4.383333333333334in"}

Figure 4.5 Reference design for the corrector.

> Table 4.2: The Size & Location of Optical Components in the Reference
> Optical Design for a 2.1 deg. Field of View Camera.

+----------------+-----------------+-------------+-------------------+
| Component      | Thickness at    | Diameter    | Distance from     |
|                |                 |             |                   |
|                | center (mm)     | \(mm\)      | Primary (mm)      |
+----------------+-----------------+-------------+-------------------+
| C1             | 70              | 900         | 9200              |
+----------------+-----------------+-------------+-------------------+
| C2             | 80              | 830         | 9275              |
+----------------+-----------------+-------------+-------------------+
| C3             | 35              | 640         | 9717              |
+----------------+-----------------+-------------+-------------------+
| Filter         | 12              | 560         | 10458             |
+----------------+-----------------+-------------+-------------------+
| C4             | 75              | 540         | 10483             |
+----------------+-----------------+-------------+-------------------+
| Window         | 24              | 480         | 10730             |
+----------------+-----------------+-------------+-------------------+
| Focal plane    |                 | 440         | 10755             |
+----------------+-----------------+-------------+-------------------+

The optical design places tight tolerances on the alignment of the
lenses.  A preliminary tolerance analysis shows that two of the lenses
must be aligned to an accuracy of +/- 30 microns in each dimension. 
This accuracy is challenging, but achievable. A preliminary quotation of
for the lenses is \$2.43M with a delivery of 2 years ARO. Another \$500K
and 6 months is estimated for building the support barrel and to
assemble and align the lenses in the barrel to the required tolerances.

***4.5.2 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 4.3.

Table 4.3 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
  ----------- -------------------- --------------------- -----------------

***4.5.3*** ***Filter*** ***Changing*** ***System***

Typically there will be one or more 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 would 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 20 sec. and it will be possible to
perform a filter change during slewing if necessary. 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. Our choice for these CCDs will be discussed in section 4.6. We
have contacted vendors to determine cost and feasibility of procuring
filters this size. Preliminary cost estimates are roughly \$100K per
filter.

***4.5.4*** ***Camera Shutter***

Another essential component is the camera shutter. It is desirable to
have a relatively fast (compared to the nominal exposure time of 100
sec) and repeatable shutter which results in equal exposure time for all
CCDs in the focal plane. Two designs are being considered. A scroll
shutter is our Reference Design and is shown in Figure 4.6. This shutter
would take a minimum of space in the cage, allowing good access to the
corrector, camera and cables. This design is also flexible in that the
opening in the scroll could be modified to give very short exposure
times. A leaf shutter would fit within the cage, but it would restrict
access to the corrector support barrel. Both shutters provide uniform
exposure for the CCDs and would include a position sensing system to
feedback the precise location of the shutter as it opens or closes.

![](media/image39.wmf){width="6.125in" height="3.9791666666666665in"}

Figure 4.6 Details of the scroll shutter design.

# 

# 4.6 CCDs , CCD packaging and Front-end Electronics

***4.6.1 Pixel*** ***Size***

The telescope seeing and the pixel size determine the size of a
point-like image. Typically this is represented in terms of the FWHM of
the point spread function (PSF). While the seeing at the site can be as
small as 0.6", the effective dome seeing with the Mosaic II and its
corrector is typically between 0.8" and 1.1". Given that the Blanco
prime focus image scale is 55 microns/arcsec, we selected a pixel size
of 15 microns ( 0.27") for our reference design so that the PSF would be
sampled over at least 2 pixels in ideal seeing (0.6") and 3 pixels in
the best recorded seeing (.8"). If smaller pixels were used to provide
finer sampling there would only be a small gain in the potential
precision of the photometry. The choice of 15-micron pixels makes a very
small contribution to the PSF width even in ideal seeing.

***4.6.2 CCD*** ***Options***

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 microns thick and the total device thickness
is often less than 50 microns. The absorption length in silicon is 205
microns at a wavelength of 1000nm and thus thick sensors are required
for a better QE at that wavelength. Photons from the near infrared
wavelengths will simply pass through 50 microns of silicon. The SNAP
project (led by LBNL) has requirements similar to ours and LBNL has
already developed thick, fully depleted, back illuminated

CCDs 200-300 um thick^1^. For our Reference Design we use 250 micron
thick LBNL CCDs. The QE of the LBNL CCDs is shown in Figure 4.7 and
compared to a standard thinned astronomical CCD and a deep-depleted
CCD^2^.

![](media/image40.wmf){width="6.395833333333333in"
height="3.428472222222222in"}

Figure 4.7 QE curves from different types of CCDs.

LBNL has produced a variety of CCD arrays for different projects and
several devices have already been deployed for astronomical use. Figure
4.8 shows a 6" diameter wafer with LBNL CCDs. For our reference design
we choose the 4-side buttable 2048 x 4096 CCD. These are the two largest
devices shown on the wafer. The development of this CCD is nearly
complete and they meet our specifications as shown in Table 4.4. This
CCD has 15 micron pixels and 2 readout channels. The readout time is
under investigation. These devices have been clocked at 240kps on a
probe station at -40^o^C. At this rate it would take 17.5 sec. to
readout a CCD. This is well within the 35sec slew time of the Blanco and
thus readout would not introduce additional overhead to the survey.

![](media/image41.png){width="4.002083333333333in"
height="2.817361111111111in"}

> Figure 4.8 LBNL CCDs on a 6" wafer

Although we have not ruled out using commercially available CCDs, the
overall approach in our reference design is to assume we will use the
LBNL thick CCDs. We believe this minimizes the R&D needed for this
project by using CCD designs that have already been proven. Currently no
commercially available CCDs meet our requirements on QE in the near
infrared region.

Figure 4.9 shows our Reference Design for the focal plane layout with
the 2K x 4K 4-side buttable LBNL CCDs. It contains 60 CCDs and a total
active area of 2.8 deg^2^. This satisfies the requirements described in
Chapter 3 for the camera size.

Table 4.4 CCD Specifications

+------------------------+---------------------+----------------------+
|                        | LBNL CCD            | DECam requirements/  |
|                        | performance         |                      |
|                        |                     | Reference Design     |
+------------------------+---------------------+----------------------+
| Pixel array            | 2048 x 4096 pixels  | 2048 x 4096 pixels   |
+------------------------+---------------------+----------------------+
| Pixel size             | 15 m x 15 m         | 15 m x 15 m          |
|                        |                     | (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     | 300,000 e^-^        | \>100,000 e^-^       |
+------------------------+---------------------+----------------------+
| Dark current           | 2 e^-^/hr/pixel at  | \<\~25 e^-^/hr/pixel |
|                        | --150^o^C           |                      |
+------------------------+---------------------+----------------------+
| Persistence            | Erase mechanism     | Erase mechanism      |
+------------------------+---------------------+----------------------+
| Read noise             | 2 e^-^              | \< 10 e^-^           |
+------------------------+---------------------+----------------------+
| Charge Transfer        | \< 10^-6^           | \<10^-5^             |
| Inefficiency           |                     |                      |
+------------------------+---------------------+----------------------+
| Diffusion              | 8 m                 | \< 10 m              |
+------------------------+---------------------+----------------------+
| Linearity              | Better than 1%      | 1%                   |
+------------------------+---------------------+----------------------+

**4.6.3 Front-end Electronics**

We are evaluating two options for the front-end electronics. This is the
system that takes the analog signals directly from the CCD and converts
them to digital signals. We later discuss the DAQ system that we define
as the CCD control electronics, the conversion of the digital signals to
optical and the system that interfaces with the telescope controls for
guiding and focus. The choice of a front-end electronics system affects
the CCD packaging (discussed in the next subsection).

Our Reference Design uses the CRIC2 chip that is being developed by LBNL
for the SNAP project^3^. This chip is an attractive solution because it
can be mounted in the CCD package, enabling signals to be converted to
digital at the CCD and thus minimizing the possibility of noise pickup
and also reducing the electronics needed outside the camera vessel,
inside the cage. Digital signals exit the camera vessel and are
converted to optical signals in the cage. The CRIC2 is designed to work
at the temperature of the CCD focal planes (\~ -120^o^ C). The CRIC
chip, which contains just the double correlated sampler, but not the
analog to digital converter, has already been made. It was tested at
-130^o^ C and meets our signal to noise requirements. Design of the
electronics inside and outside the vessel is being investigated.

![](media/image42.wmf){width="4.0625in" height="3.560416666666667in"}

> Figure 4.9 Focal plane layout using the 2K x 4K LBNL CCDs and 8
> additional small CCDs for guiding and focus.

Our backup front-end electronics option uses the Monsoon front-end
system (called Detector Head Electronics or DHE), which is being
developed at NOAO as the general purpose CCD readout, and control
system. With this system the analog CCD signals exit the camera vessel
via cables and feed-throughs. The signals are processed and converted to
optical signals in crates mounted in the cage. As discussed in Section
4.8 the readout for our focal plane would require 2 standard VME crates
in the cage and these would generate \~ 390 Watts of heat. For our
initial CCD test stands we will use the Monsoon DAQ system with the
Monsoon DHE. This is described in more detail in Section 4.8.

***4.6.4 CCD*** ***Packaging***

CCD packaging is the act of gluing a readout board and a
thermal/mechanical interface to the front of the CCD. A picture of a
packaged CCD is shown in Figure 4.10. The readout board is made of
Aluminum Nitride (AlN) and contains bond pads, for wire bonding to the
CCD, and a connector for a cable. A piece of molybdenum (Mo) or Invar,
called the foot, is glued to the board to provide the precision
alignment features, thermal contact with the cooling plate as well as
stiffness for the CCD package. In the case of the CRIC2 front-end
system, the readout board also contains the circuitry for operation and
control of the CRIC2 chip and the mechanical foot has to allow
additional space for the chip and circuitry.

![](media/image43.png){width="2.4895833333333335in"
height="1.6458333333333333in"}

Figure 4.10 A picture of a packaged 2K x 4K CCD^4^.

CCDs are typically operated at temperatures of -90 to -120 deg. C,
however, they must be glued to the support and readout boards 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,
development of a packaging process for the 2K x 4K CCDs is well underway
at LBNL. In particular, AlN has been selected for the material of the
readout board and Mo or Invar have been shown to be good materials to
match the thermal expansion properties of the 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 collider program. We believe that CCD packaging will
be a natural extension of that experience. In the next few months we
would like to begin pursuing CCD packaging at Fermilab with the 2K x 4K
CCDs and the Monsoon front-end electronics. In Section 4.9 we outline
this program and the resources needed to support this effort. Our aim is
to be ready to place a production order for the CCDs by June of 2005.

***4.6.5*** ***Focus*** ***CCDs***

Focusing of the Blanco with Mosaic II is somewhat time consuming (\~10
min) and thus is typically performed only 2 or 3 times per night. It
involves taking a series of images and determining which has the best
focus. We plan to design a semi-automatic focus system that can be used
more often, without adding to the observing overhead. This consists of
two sets of focus chips installed above and below the ideal focal plane.
As the telescope drifts out of focus for the image CCDs, the focus in
one or the other set of focus chips will get better. The focus chips are
readout with the image CCDs. The focus is thus monitored on every image
and can be adjusted as necessary.

As discussed in Chapter 3, an area of \~40 sq. arcmin would typically
have \~three bright stars which are useable for focusing. The vertical
offset of the focus CCDs with respect to the focal plane will depend
somewhat on the corrector optics and the desired defocusing of the
images. Using the f-ratio of the primary and the assumption that we want
a 0.7 arcsec image to double, we find that the focus CCDs should be
\~200 microns from the best focus of the focal plane. LBNL has developed
a variety of small CCD options. For our Reference Design we have
selected a 982x935 device with 15 micron pixels. Each CCD covers 18.6
sq. arcmin and has two readout channels. Four of these CCDs would be
used for the focusing system.

***4.6.6 Guide CCDs***

The Mosaic II has a 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. In our reference design we have CCDs on the focal plane devoted to
guiding.

Guide CCDs have two requirements in addition to those listed in Table
4.4. 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 4.8.

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.
The image can be transferred very quickly (\~1ms) from the image side to
the storage side. This minimizes smearing the image during the readout.
Once an image is on the storage side, the image side is erased, a new
image is initiated and the readout can occur from the storage area of
the CCD.

The CCDs described above for focusing are also 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 240kpix/sec would result in a readout time of \~0.9 sec.
Figure 4.9 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 for redundancy.

# 4.7 Camera Vessel and Cooling

This section describes the vacuum vessel that houses the CCD focal plane
and all the associated cooling and mechanical components. The CCDs will
be precisely located and attached to a large metallic plate called the
focal plate. This plate defines the interface between the CCDs and the
camera vessel. To achieve the low noise necessary for astronomical
applications, the CCDs are typically cooled to -90 to -120 deg. C. For
our Reference Design we assume they need to be cooled to -120 deg. C,
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 an LN2 volume (the cryostat) internal to
the vacuum vessel. Small heating elements will be used to actively
regulate the temperature of the focal plate. Good vacuum in the vessel
is needed to minimize the thermal load from the ambient environment.

Figure 4.11 shows a sketch of the layout of the cooling system within
the vacuum vessel. The vacuum vessel (the camera) will be mounted on the
end of the corrector. Mechanical support of the focal plate will be from
the window end of the vacuum vessel; thermal insulators will be used to
minimize the heat transfer from the vessel walls to the focal plate. The
heat load on the cooling system comes primarily from the ambient
environment through the vessel window (\~100 W), and the CCD's and the
associated electronics (\~20 W). Feed-throughs and resistive losses
along the cables will add an additional load. For our Reference Design
we have chosen a cooling system capable of removing 200 W from the
vessel.

The cryostat shown in Figure 4.11 would be either continuously or
automatically batch filled. Two inputs and outputs will be located in
the cryostat to allow proper filling and venting at the different
telescope angles. The LN2 will be delivered to the prime focus cage in
vacuum insulated piping with flexible joints to allow for the rotation
of the cage.

![](media/image44.png){width="3.9680555555555554in"
height="3.295138888888889in"}

> Figure 4.11 Schematic view of the Dark Energy Camera Vacuum vessel.
> The heat load from the cables is intercepted at the cold station near
> the LN2 vessel. Zeolite is a getter material which will help maintain
> a good vacuum. The two sets of supply and return lines are located at
> different heights to accommodate the tipping of the telescope.

Although liquid nitrogen is available from the plant at the Blanco, we
are pursuing using a nitrogen recondenser system. A schematic of the
system is shown in Figure 4.12. A helium cold finger pulse tube is
mounted to the top of each recondensing dewar. The returning warm gas is
delivered to the cold finger and condenses on it, dripping down into the
recondensing dewar. The liquid nitrogen supply to the camera comes from
the bottom of these dewars. We estimate two 210 liter recondensing
dewars are needed. This system has no moving parts in the primary cage,
a big advantage in terms of operations, maintenance and minimization of
vibrations. The helium compressors and the recondensing dewars would be
mounted either on the floor of the telescope platform, or in the area
around the Cassegrain focus. A backup dewar would be used in case of
failures. Ideally this system would be filled with LN2 only once and
require little or no refilling. Discussions with NOAO are in progress to
determine possible locations for the recondensing dewars, and the length
and paths of the piping.

![](media/image45.png){width="2.6256944444444446in"
height="3.248611111111111in"}

> Figure 4.12 The schematic layout of the LN2 recondenser system with
> two 210 liter recondensing dewars and one passive 180 liter back up
> dewar for emergencies.

# 4.8 Data Acquisition

This section describes the data processing system that resides outside
the vacuum vessel. The interfaces to this system are the cable
feed-through ports on the vacuum vessel and the optical cables/signals
that are delivered to the data management system in the telescope
control room.

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 and NOAO. NOAO has
developed a data acquisition framework, called Monsoon^5^, which we
propose to adapt to this project. This approach carries two advantages
over a 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.

## *4.8.1 Data Acquisition Architecture*

Our Reference Design uses the CRIC2 chip for the front-end electronics.
The CRIC2 is mounted directly on the CCD package and converts the CCD
signals to digital before they exit the camera vessel. However, for the
upcoming R&D efforts, we will employ the Monsoon system with its
detector head electronics (DHE) instead of the CRIC2 chip. Development
of Monsoon DHE system also serves as a fall back data acquisition system
in case the CRIC2 is not available on our time scale. We take the CRIC2
as our Reference Design because it significantly reduces the electronics
mounted in the primary cage. The heat from the front end electronics
must be minimized and dissipated carefully in order to avoid creating
thermal gradients (and thus distortions) in the primary light path.
CRIC2 also has the advantage of converting the analog CCD signals to
digital right at the CCD, thus minimizing the chances of noise pickup in
the cables. The CRIC2 system has sufficient advantages, particularly for
a large mosaic array such as the one we are proposing, that we hope to
be able to use it in our camera. Below we will first describe the DAQ
architecture with the CRIC2 and then discuss our testing and backup
solution with the Monsoon DHE.

The DAQ architecture with the CRIC2 is shown in Figure 4.13 and the
important parameters are listed in Table 4.5. Figure 4.14 shows the
architecture with the Monsoon DHE. The data rate is high by astronomical
standards, but is not beyond what particle physics experiments routinely
achieve. 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.

Table 4.5 Data Acquisition Parameters

+-------------------------------+--------------------------------------+
| ### Image CCD Array           | ###                                  |
+-------------------------------+--------------------------------------+
| > Number of CCDs              | 60 (+4 focus CCDs)                   |
+-------------------------------+--------------------------------------+
| > Pixels per CCD              | 2048x4096                            |
+-------------------------------+--------------------------------------+
| > Amplifiers per CCD          | 2                                    |
+-------------------------------+--------------------------------------+
| > Pixel digitization rate     | 240 kHz                              |
+-------------------------------+--------------------------------------+
| > Digitization time           | 17.5 s                               |
+-------------------------------+--------------------------------------+
| > Bytes per image             | 1.0x10^9^                            |
+-------------------------------+--------------------------------------+
| > Image data rate             | 10 MB/s (@ one image per 100s)       |
+-------------------------------+--------------------------------------+
| ### Guide CCD array           | ###                                  |
+-------------------------------+--------------------------------------+
| > Number of CCDs              | 4                                    |
+-------------------------------+--------------------------------------+
| > Pixels per CCD              | 982 x 935                            |
+-------------------------------+--------------------------------------+
| > Amplifiers per CCD          | 2                                    |
+-------------------------------+--------------------------------------+
| > Pixel digitization rate     | 240 khz                              |
+-------------------------------+--------------------------------------+
| > Digitization time           | 0.9 sec                              |
+-------------------------------+--------------------------------------+
| > Bytes per guide             | 5.8x10^6^                            |
+-------------------------------+--------------------------------------+
| > Guide data rate             | 5.8 MB/s (@ one guide per second)    |
+-------------------------------+--------------------------------------+

The DAQ will accept digitized CCD pixel information and deliver it to
permanent storage for further processing. Real time processing will
include using guide stars to control the telescope pointing. There will
be two readout sequences, one for the image and focus CCDs and one for
the guide CCDs.

During construction, the DAQ will be used as a test stand, first to test
individual CCDs as they are fabricated, then to debug the focal plane
before installation on the telescope. Thus, the DAQ will be completed
well before installation.

# *DAQ Implementation with CRIC2*

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 on the focal plane, by the
    LBNL CRIC2 electronics.

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

> ![](media/image46.wmf){width="4.876388888888889in"
> height="4.397222222222222in"}

## Figure 4.13 Block diagram of data and control signal flow for our Reference Design. The CCD signals are converted to digital with the CRIC2, inside the camera vessel. 

## 

## *4.8.2.1* *Hardware*

Although we will use the CRIC2 for the front end electronics, the rest
of the Monsoon configuration will need to be implemented. 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.

The use of the CRIC2 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. We will
design a system that is safe against lightning and other electrical
supply interrupts

> ![](media/image47.wmf){width="4.604166666666667in"
> height="4.529166666666667in"}

Figure 4.14 DAQ architecture with Monsoon DHE

## 

## *4.8.2.2* *Software*

We must adapt the existing software 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 centroids 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.

***4.8.3 Backup design: DAQ Architecture with Monsoon***

Monsoon is a working data acquisition framework and implementation, and
is already being implemented for use in the NEWFIRM project. We could to
use it with minor modifications. The most significant would be the use
of higher density ACQ cards, currently under development. The existing
ACQ cards have 8 channels each. Our space estimates assume 16 (but we
assume no cost or power reduction). The higher density is needed to fit
the DHE electronics into two crates on the telescope, where space is at
a premium. In order to make two crates meet our needs, one crate must
have a split backplane. This allows the guide CCD DAQ to share a crate
with the image CCD DAQ. The cost of the DAQ is calculated by scaling the
NEWFIRM channel count and including the modifications described above.
The total power is estimated at 390 W and this needs to be dissipated in
the primary cage. The total cost for an implementation for our the
backup DAQ architecture is \$280K.

# 

# 4.9 Resources Requests

We have presented our Reference Design for the Dark Energy Survey
Instrument. This would replace the existing cage on the Blanco 4m
telescope and everything inside it as well as the interfaces from cage
to the off-telescope systems. Our Reference Design meets the technical
requirements of our science program and its major characteristics are
summarized here:

1)  a 2.1 degree angular field of view corrector

2)  a set of four filters in the g, r, i and a modified z bands

3)  high purity thick CCDs which have a high QE in the i and z band

4)  a focal plane of 60 2K x 4K CCDs with a pixel size of 15 microns and
    2 readout channels per CCD

5)  Guide and focus chips (4 each) on the focal plane, these are 982 x
    935 and have a pixel size of 15 microns

6)  Use of the CRIC2 for front-end electronics with a Monsoon DAQ system

7)  A cooling system for the CCDs which uses a liquid nitrogen
    recondenser

8)  Packaging of the CCDs will occur at Fermilab.

While not required by the science goals, the decision to package the
CCDs at Fermilab gives us more control over the cash flow and schedule
than if the CCDs were packaged elsewhere and capitalizes on the
experience and facilities already in existence at SiDet.

We do not expect to change our science goals, nevertheless some of the
choices that were made to implement them are not final and may change,
particularly if we discover faster and less expensive solutions.

We have developed a preliminary schedule based on physicist and
engineering estimates for the Reference Design. The project summary
information is given in Table 4.6. The material and service costs of the
resource estimates are given in FY04 dollars and the estimate for the
effort is given in terms of FTEs. The latter is further divided into
skill types such as engineers, scientists, and technicians as shown in
Table 4.7. The preliminary estimate of the project duration is \~ 3.5
years. We define one FTE-yr to be 2000 hours of effort per year and note
this does not include any efficiency factors or contingency. While we
believe that the WBS for the Dark Energy Survey Instrument is reasonably
complete, the cost and labor estimates are preliminary. For this reason
we present our base estimates without contingency. We expect to improve
the accuracy of these estimates once we have finished a complete
preliminary engineering design. Our goal is to complete the initial pass
on this design in May of this year and to have an external review of the
project soon thereafter.

Table 4.6 Preliminary WBS for the Dark Survey Instrument

  --------- ------------------------------ ---------------- ---------------
                                           **M&S (\$K)**    **Labor
                                                            FTE-yr**

  **1.2**   **Dark Energy Instrument**     **\$6,672.00**   **32.5**

  1.2.1     CCDs and Test Stations         1,347.00         2.6

  1.2.2     CCD Packaging and Testing      592.00           11.4

  1.2.3     Corrector and Cage             3,659.00         4.9

  1.2.4     Camera                         590.00           6.9

  1.2.5     Data Acquisition               334.00           1.6

  1.2.6     Final Assembly                 130.00           4.8

  1.2.7     Models                         20.00            0.4
  --------- ------------------------------ ---------------- ---------------

Table 4.7 Preliminary Labor Estimates for the Dark Survey Instrument

  ------------------------------------ ----------------------------------
  ** **                                **Labor (FTE-yr)**

  Electrical Engineer                  3.0

  Mechanical Engineer                  8.2

  Scientist                            13.7

  MechTech                             5.7

  Draft                                1.3

  ElecTech                             0.7
  ------------------------------------ ----------------------------------

The main cost and schedule drivers are the optical system, which amounts
to \$3.7M, and the CCD procurement and packaging, which sums to \$1.9M.
We estimate that each will take approximately two years to complete. The
optical system and the CCDs currently define two technically independent
critical paths in the schedule and we hope to discover less expensive,
quicker ways of implementing our technical requirements. Since we
recognize that minimization of the cost of this project is critical to
its timely completion, we have developed a plan for optimizing our
design. This is described in section 4.9.1 along with a summary of the
resources needed to initiate this plan in the next 3 quarters (Table
4.8).

> ***4.9.1 Design Optimization Program***

This section identifies cost and schedule risks in the project and
outlines our plans to minimize them. We will be investigating our
options for simplifying the design and are avoiding any research and
development.

> ***4.9.1.1 Optical design***

Two preliminary designs for a 2.1 degree corrector were commissioned by
NOAO and one of these has been adopted for our Reference Design. For
this design we have received one preliminary quotation of \$2.53M for
the lenses with a 2 year delivery. The aspheric surfaces in this design
are responsible for both the cost and the extended schedule. In the next
few months we plan to obtain quotes from other vendors and to explore
alternative designs with the goal of reducing the cost without
sacrificing functionality. While some optical expertise exists within
our group, it is in our interest to establish a contract with an optical
designer/engineer as soon as possible. This designer/engineer would be
supervised by the leader of our optical design coordinator (S. Kent).
Together they will work though possible changes to improve and simplify
the design. Another important aspect of this analysis is a full
tolerancing of the design. This will provide specifications for the
lens-to-lens alignment and have a significant impact on the cost of the
lens support structure. Very preliminary estimates for the corrector
barrel are that it will take \~\$500K to build and \~6 months to
assemble and align the lenses in the barrel once they are completed.

The long lead-time for the corrector is a significant schedule risk for
the project. To reduce this risk we would like to get a rapid start on
the optical design by establishing a contract with an optical designer
beginning in May 2004. Our understanding is that the first 6 months of
the 2 year corrector delivery is to obtain the rough pieces of glass.
The remaining 1.5 years are for grinding, polishing and testing. We
would like to order the glass for corrector lenses (\$320K) in Nov. 04,
after a complete review of the corrector and barrel designs.

***4.9.1.2 CCDs and CCD packaging***

As discussed in section 4.6, we need high purity, thick CCDs to obtain a
high QE in the z band. Currently no commercial vendor is offering such a
product. However, LBNL has developed CCDs that meet our needs. These
have already been produced and packaged in small quantities^4^ and are
in use in telescopes at Kitt Peak National Observatory with great
success. LBNL has established a production model for CCD fabrication in
which the silicon wafers are manufactured at a commercial vendor and
then delivered to LBNL for final processing. We have initiated
discussions with LBNL about the CCD procurement and delivery and believe
we have reached a good first draft of the cost and schedule. In our
model, LBNL would receive the wafers from the foundary, perform the
final processing and then ship unpackaged, untested devices to Fermilab
for packaging and testing. We have avoided extensive development on the
CCDs by basing our plans on the existing 2K x 4K design. However, the
CCDs have a lead time of approximately 10 months for delivery of
processed unpackaged CCDs. Our experience with the construction of
silicon vertex detectors leads us to believe that it will require a
modest dedicated effort to develop CCD packaging skills and techniques
at Fermilab and to develop an efficient CCD packaging production line.
We would like to 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: May 04, order mechanical parts for packaging studies, a few
    unpackaged electrical CCDs and one fully packaged CCD, \~3 m
    delivery

-   Phase B: July 04, order preproduction parts and process \~6 wafers,
    est. 10 m delivery

-   Phase C: June 05, process the remaining wafers, \~3 m delivery

-   Phase D: June 05, place production order, \~9 m delivery

Phase A is the purchase of large number of mechanical CCDs (blank
silicon with the top layer of metal for wirebonding studies), as well as
one packaged CCD and a small quantity of unpackaged devices. We would
like to proceed with this purchase in May 2004 so that we can begin to
develop our packaging and testing facilities at SiDet.

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 x 4K CCDs have only two of these devices per wafer. The
new mask would use the same, proven, 2K x 4K CCD design and would have
four devices per wafer. Our goal is to start the new mask design in May
2004 and place the preproduction wafer order in July 2004. The delivery
schedule is \~10 months after the masks have been approved by the
foundry. On receipt of these wafers (\~April 05) we would have LBNL
process only \~6 for use in our CCD packaging studies. These would be
evaluated and tested with the goal of placing the production order in
June 05. The remainder of the wafers would be set aside for phase C.

In Phase C we would authorize LBNL to process the wafers that were set
aside in Phase B. Since the wafers would already be at LBNL, the
delivery of these would be quick, \~3 months ARO. These CCDs would
arrive at FNAL in \~Oct. 05 and would be used to start the ramp up to
full production packaging. We estimate that 10 wafers would be left,
yielding \~40 untested unpackaged CCDs. Our goal would be to package
these at an average rate of \~2/week. We will also investigate the value
(and potential losses due to handling) of performing initial tests on a
-40 deg. C probe station (with the existing equipment at SiDet) to
eliminate obviously bad CCDs before packaging.

The final phase, D, is the production run of 72 wafers. Unless
significant problems were found from the preproduction run, this would
use the preproduction wafer mask. For our initial planning purposes we
assume that we will need to receive and package a total of 160 CCDs from
LBNL to allow for the yield (initial estimate is \~50%) and to have some
spares. This order would be placed in June 05 with the delivery schedule
estimated at 7-9 months. Initially, to minimize costs, we would have
LBNL process only 30 wafers. Once full production has been established,
we assume we can package and test 120 CCDs at a rate of 4/week. The
Camera Team anticipates having the CCDs ready to mount on the focal
plane in Feb. 2007 with the goal of having the camera ready for first
light in Chile in Jan.2008.

For development of the packaging process we will need nominal support
for engineering, designing and implementing the concepts. This includes
purchasing adhesives, samples of Invar and Molybdenum as well as gluing
and wirebonding fixtures. In order to prove our packaging techniques are
successful, we must operate the CCDs at cryogenic temperatures. This
will allow us to observe possible stress points or defects in the
process. For this we need a cryostat, a cooling system and the
appropriate data acquisition system. We are also investigating a warm
(-40 deg. C) CCD testing setup. This would allow faster turn around, as
the cool down time would be significantly reduced. Accumulation of this
apparatus and knowledge gained from operating CCDs could also be of
benefit to other projects at Fermilab; both the LHC and the NLC are
investigating CCDs as a possible technology choice for a vertex
detector.

***4.9.3 Design Optimization costs for the next 3 quarters***

In Table 4.8 we show a breakdown of the M&S costs we will incur in the
next three quarters, and Table 4.9 shows a breakdown of the labor by
type. To estimate the number of FTEs, we assumed 500 hours/quarter; this
does not include any efficiency factors or contingency. These tables
cover the labor and costs of preparation of the proposal for NOAO as
well as for initiating a contract with an optical designer, placing the
CCD purchases and developing a CCD packaging and testing setup as
described above. Our schedule for the optical design and the CCDs is
aggressive in terms startup and placing the orders on a short time
scale, but it is conservative in terms of technology choices. In
particular, the CCD plan outlined above has built in flexibility later
in the project with the use of the wafers that remain from Phase B and
from the production order.

**Table 4.8 Detailed cost Breakdown for FY04 and the First Quarter in
FY05**

  --------- --------------------------- ------------- ------------ ------------
            ** **                       **FY04 - Q3   **FY04-Q4    **FY05-Q1
                                        (\$K)**       (\$K)**      (\$K)**

  **1.2**   **Dark Energy Instrument**  **105.43**    **218.88**   **512.11**

  1.2.1     CCDs and Test Stations      77.41         177.11       11.31

  1.2.2     CCD Packaging and Testing   10.02         15.27        35.56

  1.2.3     Corrector and Cage          0.00          13.33        359.10

  1.2.4     Camera                      0.00          4.17         18.59

  1.2.5     Data Acquisition            13.00         9.00         79.13

  1.2.6     Final Assembly              0.00          0.00         0.00

  1.2.7     Models                      5.00          0.00         8.42
  --------- --------------------------- ------------- ------------ ------------

**Table 4.9 Preliminary Estimate of Labor Resources**

  --------------------------- -------------- -------------- --------------
  **Labor in FTEs**           **FY04 - Q3**  **FY04 - Q4**  **FY05 -- Q1**

  Electrical Engineer         1.0            1.2            1.3

  Mechanical Engineer         1.9            2.5            2.5

  Scientist                   2.1            3.7            4.4

  MechTech                    0.3            0.9            1.1

  Draft                       1.3            1.0            0.9

  ElecTech                    0.1            0.2            0.5
  --------------------------- -------------- -------------- --------------

**4.10 Summary**

We have described a preliminary Reference Design for the Dark Energy
Survey Instrument that meets our technical specifications and presented
estimates of the cost and labor for the project. The critical path items
are the 2.1 deg FOV corrector and the CCDs. In the next few months we
need to refine our optical design and begin to research the issues
related to reading out, packaging and procuring the CCDs. Constructing a
CCD focal plane is similar in many ways to the construction of a silicon
vertex detector and the facilities at the Silicon Detector Facility
(SiDet) are well matched to these purposes. Initiation of this project
will require resources both in terms of labor and the purchase of some
equipment as outlined above. These purchases could benefit future
efforts at Fermilab as CCDs are currently being considered for future
detectors at the NLC.

**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-Resisitivity 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.

**5.** **Data Management**

The four bands from this 5000 square degrees survey will produce roughly
100 TB of imaging data over 600 nights of observing. These data must be
efficiently reduced, analyzed for the constraints on dark energy,
archived and made available to the public astrophysics community.

The survey data management includes:

-   moving data from La Serena to processing and archiving platforms,

-   ingesting data into an archive,

-   processing the data within an automated pipeline,

-   formatting, organizing, and transmitting datasets for long-term
    storage and near-term access,

-   presenting the data to users via an archive interface, and

-   reviewing data products for quality and scientific integrity.

We consider it our obligation to make our data available in a form that
will benefit the astronomy community at large.

The University of Illinois Astronomy group, which will be supported by
the National Center for Supercomputing Applications (NCSA), proposes to
take the leadership of data management and archiving. The data
management endeavor as a whole will be collaborative, involving
Fermilab, University of Chicago, NOAO and LBL. 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.

Fermilab will play a major role in supporting data management,
specializing in the following areas:

-   Data simulation and system verification via a mock data challenge.

-   Algorithms for producing science data products (particularly in the
    area of galaxy cluster detection and photometric redshifts).

-   Grid-based processing framework.

This work will be done in close collaboration with UIUC and our other
partners, sharing development and leverage the expertise and resources
at all sites.

**5.1.** **Requirements**

***5.1.1*** ***Data Products*** ***and Data Storage***

The raw data products coming from the telescope will be images dumped
from the CCDs in the camera after exposure. 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 calibrated images
themselves will be of value to the astronomy community as the basis for
research that outside the scope of our science goals; thus, they can be
released as a "level 0" product.

The primary product that will form the foundation for the science---the
level one product---will be a set of four images, one for each band,
with single image scale calibrations, covering the entire survey region.

The level two product will be the data formed and calibrated from the
co-addition/mosaicing of all the data from the survey. From these
images, we will produce a set of higher level (level three) products,
primarily catalogs that will be the subject of scientific analysis. Most
notable of these catalogs will be a full object catalog (listing
photometry and positions for all detected objects in the survey), a
photometric-redshift catalog (containing photometry and redshifts for
all galaxies detected), and a galaxy cluster catalog; these form the
level 4 products.

Experience from the Sloan survey shows that data processing actually
expands the data collection dramatically rather than reduces it. We
estimate that after the first year we will need to house roughly 100 TB.
By the end of the project, this will approach 1 Petabyte, though much of
this can reside in secondary storage (i.e. tape).

***5.1.2*** ***Processing*** ***Requirements***

Our initial estimates of the processing needed to produce our various
data products are modest: approximately 10000 hours on a highly parallel
platform. An additional factor of 2 maybe necessary to account for
reprocessing to correct deficiencies discovered in the course of
observations. However in addition to this base level necessary to
produce our science data, a significant amount of processing---roughly
of the same order---will be necessary during the software development
phase. Simulations of the data will be critical for design and testing
of the processing software. We estimate that we will need access to the
order of 100 compute nodes during both the simulation and production
processing phases of the project.

# 5.2 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 5.1 illustrates the overall data flow. All products---raw and processed---will be archived at NCSA. Our model allows some or all of the archive to be replicated at Fermilab. Similarly, while we expect that the NCSA processing resources (see section 5.3) 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 may be important in the event that significant reprocessing is needed. Our initial requirement for grid architectures is aimed at the support simulations that will be produced at Fermilab and reduced at NCSA. 

***5.2.1 Processing Framework***

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 contexts---that is, less dependent on where they
actually get run.

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. This will further minimize dependencies
on the compute platform. Several of us in the collaboration have
experience with a virtual data model for computations which should apply
well to our application. In the science domain, we should 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.

The "data management" layer will be a grid-based system, leveraging the
existing and emerging solutions from the Open Science Grid Initiative.
The major virtue of a grid-based architecture is that 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.

2.  ***Work Packages***

Below is an outline of the software development (shared across the
collaboration) that will be necessary to build our architecture.

**[On/Near Site:]{.underline}** [Operation Software (10-12 FTE
months)]{.underline}

**Scheduling:** (FTE-months: 4-6 month) tools for managing observing
programs

**Archiving:** (FTE-months: 4 months) ingest raw data into archive,
producing level 0 data

**Quality Assessment:** (FTE-months: 2 months) automated measures of
quality

**Quick Alert Processing:** (FTE-months: 0 months-CTIO/SCP
collaboration) early detection of time domain phenomena

**[Archive-based Data Access:]{.underline}** [(12 to 15
FTE-months)]{.underline}

**Standard Collection Access: (**FTE-months: 6+3/6 months**)**
facilities for browsing and accessing data products.

**Archive Replication: (**FTE-months: 3 months**)** processes for
replicating large portions of the archive

**[Processing Pipelines:]{.underline}** [(47 to 51
FTE-months)]{.underline}

**Grid-based Pipeline Framework:** (FTE-months: 9 months) infrastructure
for executing pipelines

**Simulation and System Verification:** (FTE-months: 2+6 months)
infrastructure for verifying correct data handling

**Single-Frame Calibration Pipeline: (**FTE-months: 12 months) creation
of calibrated images with astrometric solutions from a single scan in
each of four colors, producing the level 1 data

**Co-add Pipeline: (**FTE-months: 12 months) cumulative combination of
calibrated, single-frame images into four deep image databases of the
sky, producing the level 2 data

**Source Extraction Pipeline: (**FTE-months: 12 months**)** creation of
source catalog containing astrometry and photometry for all detected
sources, producing the level 3 data

**[Science Analysis Pipelines:]{.underline}** [(31
FTE-months)]{.underline}

**Photometric Redshift Pipeline: (**FTE-months: 3 months**)** provide
four-color photometry and photometric redshifts for all detected
galaxies

**Cluster-finding Pipeline: (**FTE-months: 12 months**)** creation of a
multi-frequency galaxy cluster catalog

**Weak Lensing Analysis Pipeline: (**FTE-months: 12 months**)** creation
of shear maps

**Synoptic Analysis Pipeline: (**FTE-months: 4 months + CTIO/SCP
collaboration**)** time-domain-based analysis

**[Software Engineering:]{.underline}** [(9 FTE-months)]{.underline}

**Software Development Framework: (**FTE-months: 6 months**)**
documenting and managing the software process

**Software Repository:** (FTE-months: 3 months) initialize and maintain

**5.3** **System and Software Development**

***5.3.1.*** ***Management of the Process***

Because development will shared across the partnership (primarily UIUC
and Fermilab), it is important to establish a working structure that
makes leverages the distributed expertise effectively and efficiently.
To this end, we will adopt a working group model for guiding the
development of the system. Managing the overall effort will be a Project
Manager specifically for the data management effort, who will lead the
day-to-day operations. Another key player will the Project Scientist,
who will be responsible for ensuring that the software meets the
scientific requirements. Assisting these two will be two software
working groups: the Data Management Steering Committee (DMSC) and the
Computing Working Group (CWG). Membership need not be strictly
controlled, but each should have representation from each of the partner
sites at levels appropriate to their overall contribution to the
project.

The DMSC will be made up of leaders in the project from each site. They
set the overall roadmap for data management, define the system
requirements, and work out the high-level design. In contrast, the CWG
is made up primarily of programmers and system specialists that will be
involved in the detailed development of the various subsystems. The DMSC
is expected to be most active in the early stages of development. The
CWG will be formed later once the project is up and running; at that
point, the main design activity will shift to the CWG with the DMSC
playing more of an oversight role. Given the overall size of the
project, it is likely that the two groups will share membership.

***5.3.2 Software Engineering***

The DMSC will develop the detailed requirements, from which they will
develop a high-level design. From that, a set of work packages will be
defined that define modules that make up the system. The work packages
will then be assigned to appropriate people in the collaboration. The
work package leaders will be responsible for developing a more detailed
design for the module and overseeing its development. Actual development
may involve multiple people across the different sites.

The DMSC will define a software review process that allows all partners
to review and contribute to the design in an efficient way. The review
process should be lightweight to ensure sufficiently rapid development.
The review process will take place during the regular telecons of the
working group (early on in the DMSC, later in the CWG).

***5.3.3 Software Management***

All code will be shared through CVS (or some other appropriate
network-aware revision control system).

The DMSC will define a standard reference software environment used to
develop the necessary software systems. Any software module must build
and run in this reference environment. The development environment
should be a close match to the expected production platforms and be
reasonably simple to acquire on common developers\' platforms.

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. If the reference
environment is simple to assemble, the code---particularly the pipeline
software---will be more portable to other sites.

4.  ***Data Release Strategy***

We will release raw and reduced single images, the level zero and level
one data, after a proprietary period of 12 to 18 months from the night
the data are acquired. This will occur in an automated fashion built
into the public data access tools. Search tools will enable users to
determine which data exist for a particular portion of the sky, listing
acquisition dates, minimal quality information, and then allowing
downloads of data that are older than the proprietary period.

The co-added database and object catalogs, the level two and three data,
will be released in two stages. The first stage will occur at the end of
the third year of the survey, when data from the initial, shallow survey
over the entire SPT region will be released. The second stage will occur
at the end of the sixth year (i.e. one year beyond the end of the
survey), and these data will essentially serve as the final data
release. 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.

3.  **The Role of Fermilab in Data Management**

The Experimental Astrophysics Group and the Computing Division at
Fermilab expect to make a substantial contribution of expertise and
resources to this project. We expect to take the lead on several of the
packages listed in section 5.2.2 and be major partners in more of them.
This will be done in close collaboration with our colleagues as
described in sections 5.2 and 5.3.

A major focus in the Fermilab effort in data management will be in
creating the simulations necessary to provide a useful mock data
challenge in 2007-2008. We expect this data set to be on order of the
size of data we will obtain in our first year of operation. We expect to
create the simulation data using Fermilab and grid resources, and to
share these simulations with our partners at NCSA using grid storage
resource management tools. These contributions will serve the dual
purpose of 1) testing and verifying the data handling system from the
end of the data acquisition system to the standard collection access
methods, and 2) developing the grid-based pipeline framework and the
archive replication processes. We could contribute our software
repository expertise.

The Experimental Astrophysics Group expects to take the lead on the
development of the co-add pipeline for the Processing Pipelines effort,
based on current research in the co-addition of the five bandpass,
200-sq degree area of the SDSS Southern Survey. The Experimental
Astrophysics Group also expects to take the lead on the development of
the photometric redshift and cluster finding pipelines for the Science
Analysis Pipelines, based on the research interests of EAG scientists.
The processing code, and the science analysis pipelines in particular,
are understood to be an collaborative efforts amongst the survey
scientists, with the development process extending across institutional
boundaries inside the survey.

**5.5** **Resources**

***5.5.1 Personnel***

A current estimate of the labor requirements for this project can be
gotten by summing the estimates for the individual packages listed in
section 5.2.2. This yields 109-118 FTE months (9-10 FTE years). Much of
this activity should take place during the camera building (four yrs),
and a lower level of effort will continue during the survey (five yrs).
Given the early stage of this design, we believe it is prudent to double
these estimates for a more realistic measure of the personnel required;
in other words, we estimate needing approximately 4 FTEs per year over
the five years.

NCSA and the UIUC administration has committed support for 1 FTE of a
research programmer or astronomer to work on the Dark Energy Survey.
Additional support will be sought from the National Science Foundation
in a grant based at UIUC. In addition, there will be 6 FTE working on
the design and implementation of a data management strategy for the
Large Synoptic Survey Telescope. The Dark Energy Survey data management
component will benefit from this parallel effort together with the
existing NCSA experience in archiving, analyzing, visualizing and
serving to the public of large astronomical datasets.

At Fermilab, we will need two computing professionals to support the
grid management systems, maintain the storage systems, and develop
grid-based processing infrastructure. This includes the integration of
workflow management, virtual organization, and replica management tools.
We will also need one full-time professional astronomer to lead
development of data simulators and develop calibration and science
analysis algorithms. We will also leverage the general participation of
the EAG to support the science and the Computing group to support the
hardware infrastructure. The computing professionals are expected to
become necessary in stages during 2006 and 2007.

***5.5.2 Hardware***

NCSA will provide the hardware required for the data management task at
the University of Illinois. This will be 100TB of disk space and 500TB
of fast tape storage. CPU needs will be met on a range of machines
through a minimal, guaranteed allocation of 10,000SU/yr and the option
to propose for more resources as needed. These resources are routinely
awarded cost-free within competitions. The emphasis on data management
at NCSA for the long term will make it straightforward to access the CPU
cycles needed for the data analysis. These resources and their
maintenance will be provided by NCSA at no cost to the project.

At Fermilab, we estimate that we will need 20 TB by 2006 and 100 TB by
2008, initially to support system simulations and system verification
but also, after observations begin, to photometric calibration efforts
and quality assurance. This must be backed by a larger amount of Enstore
tape space; by mid 2008, we expect to harness 500 TB of tape storage. We
will need an order of 100 compute nodes by mid 2007 (either in the form
of a group cluster or as a component of the Fermilab Farms system) to
support simulation work. Later, this can be harnessed to augment general
processing as well as detailed science analysis.

**6. Project Management Structure and Near Term Goals**

This chapter describes our project management structure that has allowed
us to initiate the survey planning and conceptual design, and to prepare
this proposal. It is a simple structure and it has served our needs. In
November, we formed a Management Committee (MC) to guide our overall
effort and in January, we formed the Dark Energy Camera team to design
the Camera. More recently we formed the Science team and the Survey
Strategy team. The remaining sections describe these teams and their
immediate work plans. This Chapter also describes the prior, relevant
experience of the participants.

1.  **Management Committee and its Functions**

Each Management Committee (MC) member has specific responsibilities. The
MC, with other participants, meets weekly by videoconference in order to
track progress and identify critical items. About twenty scientists were
engaged in the preparation of this proposal, the formulation of the
science program, and the design of the camera. We expect the number will
grow somewhat in the future since two institutions joined in March. The
membership of the MC is as follows:

> J. Annis Camera Project Scientist
>
> R. Brunner NCSA liaison
>
> B. Flaugher Camera Project Manager
>
> J. Frieman Proposal Science Leader, University of Chicago liaison
>
> J. Mohr Data Management Project Scientist
>
> R. Plante Data Management Project Manager
>
> J. Peoples Management Committee Chairman
>
> J. Thaler Data Acquisition Project Manager
>
> W. Wester CCD and front-end electronics leader

The MC has at least one representative from each of the three founding
institutions and we anticipate enlarging the MC to include at least one
member from LBNL in the future. We will add a member from CTIO if our
NOAO proposal is approved. Tim Abbott is currently serving as the CTIO/
NOAO technical contact for our NOAO proposal development and he
participates in the weekly videoconferences. The MC is responsible for
System Integration and all interfaces, hardware and software, among all
institutions including CTIO and NOAO Data Products Division. The MC
integrates the efforts of the Camera Team, Science Team and Survey
Strategy Team through the participation of the team leaders in the MC
decision process.

2.  **Dark Energy Camera Team**

The Camera Team is made up of the Dark Energy Survey participants from
Fermilab, the UIUC High Energy Group, and the LBNL Cosmology and
Microsystems Groups. The Camera design effort is led by the Camera
Project Manager, Brenna Flaugher, who coordinates the efforts of the PPD
Support Departments and Technical Centers for the Camera Team, and
directs the lead scientists who are responsible for the following areas:

Camera Project Scientist J. Annis

Optical Design Coordinator S. Kent

CCD and electronics coordinator from FNAL W. Wester

CCD and electronics coordinator from LBNL N. Roe

Data Acquisition System Manager J. Thaler

Typically one videoconference meeting per week is devoted to the Camera
design and nearly all of the people working on the Camera participate.
Members of the LBNL Cosmology and Microsystems groups have begun
participating in these meetings and Natalie Roe coordinates their
participation. William Wester coordinates the Fermilab Electrical
Engineering Department participation in this effort as well. The LBNL
groups have given the Camera Team considerable guidance on how to plan
the acquisition of the CCDs and front-end electronics. Tim Abbott has
also been a frequent participant in Camera design videoconferences. In
that capacity he has provided critical information on the Blanco
performance and the Cerro Tololo infrastructure. It has also received
valuable engineering support from the PPD Electrical Support Department,
Mechanical Support Department, and Technical Centers Department in the
development of the Reference Design of the Dark Energy Camera. Brenna
Flaugher is the primary contact for the Fermilab Engineering Departments
and PPD Support Centers (such as SiDet) for the Camera. John Thaler of
UIUC leads the design of the Data Acquisition System and its integration
with Dark Energy Camera and CTIO infrastructure. His group has already
begun evaluating Monsoon architecture in order to determine how to
interface it to the two types of front-end electronics that the Camera
team is evaluating. The UIUC group plans to acquire sufficient Monsoon
hardware to build a test set up in the near future. With the exception
of an Optical Designer, the current Camera Team, including the FNAL
engineering support, has the skills and size to complete the Camera
design and initiate the CCD and front-end electronics development and
testing program.

3.  **Science Team**

The Science Team is responsible for the development of the scientific
program and science goals of the proposal. While the Science team is
open to all participants, 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.

In the near term, this team will develop a better understanding of the
requirements that the science goals impose on the science software and
the survey calibrations. They will also clarify the synergies between
the Dark Energy Survey and the future more ambitious projects such as
LSST and JDEM, as mentioned in Chapter 7.

4.  **Survey Strategy Team**

The Survey Strategy team is responsible for developing the observing
plan and assuring that it will meet the science goals. The team includes
participation from B. Flaugher, J. Frieman, J. Mohr, D. Tucker and W.
Wester and it is led by J. Annis and H. Lin. This effort includes the
photometric calibration, and 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 April 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 Camera team who will serve as the points of contact.

5.  **Data Management Team**

This team is lead by the Data Management Project Scientist and the Data
Management Project Manager. They are responsible for defining the Data
Management framework and coordinating the work of the Data Management
team. They will continue to define the Data Management tasks, including
the computing framework, data processing and archiving. Plante and Mohr
lead this effort, with contributions from Annis and Frieman. The Data
Management team will also establish the computing framework for the
simulations so that the code and results are properly archived, thereby
making them available to the entire collaboration. Additional details of
the Data Management organization are presented in section 5.3.1 of
Chapter 5.

6.  **Relevant, Prior Experience of the Dark Energy Survey
    Collaboration**

***6.6.1 Camera 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, is evaluating
whether the techniques and facilities used at SiDet for wire bonding
front-end electronics to the silicon vertex sensors can also 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 Camera 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 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 Camera.

***6.6.2 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 Digital Image Library, the BIMA
Data Archive and the BIMA Image Library. 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 reduction 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.

***6.6.3 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. 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.

**6.7** **Near Term Goals**

We have set the following goals for the four teams for the next four
months:

-   Develop a complete proposal for the Dark Energy Survey so that it
    can be reviewed by an independent group in early June and after
    revision submitted to NOAO in August;

-   Augment the optical design effort at Fermilab with a consultant to
    complete the optical design of the corrector and assure that it
    meets the science requirements and is cost effective;

-   Initiate the CCD and front-end electronics development and testing
    program;

-   Bring the data management framework to the next level of
    functionality to enable the start of simulations.

-   Bring the Reference Design to a complete preliminary engineering
    design;

In August, we must submit the proposal to NOAO to build and commission
the Dark Energy Camera if we are to participate in the competition. We
also plan to fill in the details of the data management system and the
relationship of NCSA to the computation efforts at the other
participating sites. At that time, we will also be in a position to
define the nature of the partnership with NOAO-CTIO (telescope and
observing) and NOAO-Tucson (data archiving). We will prepare a formal
project management plan as part of the proposal to NOAO. We are
persuaded that our current management structure will serve quite well at
least until we begin to build hardware and software systems.

**7.** **The Relationship of the Dark Energy Survey to other
Astrophysics Projects**

This chapter describes the commitments of the Dark Energy Project
participants to other astrophysics projects and it also describes the
temporal relationship of these projects to the Dark Energy Project
within each institution.

1.  **Fermilab Projects**

Fermilab scientists and staff are engaged in both the SDSS and SNAP
projects. The Fermilab EAG has had a major involvement in the SDSS over
the past fourteen years. The initial five-year observation phase of the
SDSS will end July 1, 2005. The SDSS consortium has developed a plan for
a three-year extension and is actively seeking funds for the extension.
While some Fermilab scientists will actively participate in the
extension, the Fermilab astronomers from the EAG who plan to participate
in the DES will reduce the level of their contributions to the SDSS
infrastructure after July 2005. Since the survey will have been in
operation for five years and its operation is mature the demands on the
Fermilab participants can be reduced. In addition, two Fermilab
scientists recently joined the SDSS supernova effort and are active
participants in planning and leading the SDSS supernova campaign. Their
participation in the SDSS will help to share the load of infrastructure
work with the EAG astronomers who plan to continue to work actively on
the SDSS extension. The EAG proposes to support the data processing of
the SDSS imaging data and to manage the full SDSS archive during the
extension. The SDSS will either 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. When that happens, the computer professionals and the
astronomers in the EAG could contribute to both the pre-launch phases of
SNAP and the operations phase of the Dark Energy Survey. The EAG
computer professionals have the ability to provide support for
simulations, observations, data processing, data analysis and archiving
once the SDSS ends. Nevertheless, the Dark Energy Collaboration does not
plan to make large demands on the EAG support team since the data
management and data distribution effort will be led by and primarily
supported by the University of Illinois.

The EAG members have been joined by several Fermilab particle physicists
to form the Fermilab SNAP group. All are engaged in contributing to the
SNAP collaboration development of the science case for 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 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 2013, the Dark Energy Survey will fit nicely in the temporal
gap between the end of the SDSS and the start of JDEM.

**7.2 University of Illinois Projects**

**ionship of the Dark Energy Survey to Astrophysical Projects at the
Other Institutions.**

One of the University of Illinois astronomers (J. Mohr) is a member of
the South Pole Telescope Collaboration, and all of the University of
Illinois members are participating 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. 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, since 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.

8.  **Conclusions**

The purpose of this proposal is to request permission for the Fermilab
team to join the Dark Energy Survey collaboration so that it can
contribute to the NOAO proposal for the Dark Energy Survey and to
participate in the collaboration program of technical design and science
analyses. The primary goal of our scientific program is to obtain a more
precise determination of the equation of state of the Universe, w,
through four independent, complementary measurements. While we intend to
focus our efforts on dark energy and dark matter, the survey data will
also provide the scientific community with data that will be relevant to
a broad range of current problems in astrophysics and cosmology. We
believe it presents a superb scientific program and if it is carried out
it will have an important impact on the science at the intersection of
particle physics, astrophysics, and cosmology. This proposal describes
our preliminary plan for building the camera and the software systems
that will be needed to support the survey science program. We are
confident that we can win the competition if the Laboratory supports the
Fermilab team at the requested level and with that support we are
convinced that we can carry out this program.

We believe the Dark Energy Survey fits well in the context of the long
term plans of the field. The Connecting Quarks with the Cosmos Report
recommended that ground- and space-based wide-field telescopes with
Gigapixel-scale cameras be pursued to address the dark energy question.
The Report highlighted two major complementary projects that have been
proposed with primary goals that include probing the dark energy. The
Supernova/Acceleration Probe (SNAP) is a satellite concept for the
NASA-DOE Joint Dark Energy Mission that will include a deep z\>1
supernova survey and a wide-area weak lensing survey. The Large Synoptic
Survey Telescope (LSST) is a proposed 8-meter telescope with a
wide-field imager that will carry out multiple scans over \~1/2 of the
sky to study cosmology, particularly via weak lensing and transient
astronomical phenomena. These projects aim to achieve exquisite
precision on cosmological parameters using complementary techniques and
in particular go deep enough to offer the possibility of constraining
the time evolution of the dark energy, provided systematic errors can be
controlled at a fine level. Designed as 'ultimate' experiments, they
involve significant technological development and necessarily long lead
times; both projects aim to begin science operations on the order of a
decade from now.

The Dark Energy Survey, by contrast, is more modest in scale, cost, and
science goals, but it will deliver excellent dark energy science on a
shorter timescale. It will serve as both a scientific and technical
precursor to these major projects and therefore constitutes the logical
next step for this field. The Dark Energy Survey will enable us to learn
much about the dark energy and about the limitations and relative
advantages of the different techniques for probing it, knowledge that
will be useful for the larger projects to follow.

Many participants in the Dark Energy Survey are participating in the
planning and design of LSST and SNAP. They are also participants in
experiments that are providing results now. The Dark Energy Survey can
be mounted soon after the current experiments finish and the observing
phase will almost be completed just as the more ambitious surveys reach
the observation stage. For these reasons we believe that the Dark Energy
Survey fits nicely between the two sets of surveys and that it will
provide a substantial advance in the precision of Dark Energy relative
to the current surveys. Moreover, as a precursor to LSST and SNAP we
anticipate substantial cross-fertilization and knowledge transfer that
will be useful for all the projects.

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 our
survey in a timely way. Our new collaborators bring technical
capabilities that more than fill those gaps. Moreover, their
participation has strengthened and enriched our scientific program. The
role of Fermilab is central to the collaboration, and if we are to
progress quickly, which is essential if we are to win the competition,
we must do the following during the remainder of this calendar year:

-   Order mechanical and electrical CCDs for packaging studies;

-   Design and build a CCD testing station;

-   Design a new wafer mask with four 2K x 4K CCDs and place the minimal
    order with the foundry (24 wafers);

-   Contract an optical designer and develop a more cost effective
    design;

-   Build a realistic model of the Blanco for integration;

-   

-   Complete the preparation of the NOAO proposal that is due Aug.
    15^th^.

In order to complete these tasks quickly and effectively the
collaboration makes the following request of the Laboratory:

-   Labor: the Fermilab group needs the full time equivalents of 1
    electrical engineer, 2 mechanical engineers, 1 mechanical technician
    and 1 design/drafter and 30% of an electrical technician for the
    next three quarters.

-   Budget: In order to obtain the needed materials and services the
    Fermilab group needs a cash budget of \$100K and \$219K in the last
    two quarters of FY04 and \$512K in the 1^st^ quarter of FY05
    respectively.

We have described these requests in much greater detail in Chapter 4 of
this proposal.

We realize that this is a significant request, but we must take forceful
steps in the beginning if we are to succeed. By moving forward
aggressively during the remainder of this calendar year we can develop a
cost-effective design that is consistent with our science goals. By the
standards of today's, or even yesterday's high-energy physics proposals,
this project requires a modest cash outlay. In order to assure that the
burden of the cash budget will not fall solely on Fermilab and DOE we
will work with our partner institutions to find ways to share the cash
burden.

The science opportunities of the Dark Energy Survey are important,
timely and directly relevant to particle physics and astrophysics. Our
collaboration has the capabilities needed to build the camera, manage
the data, and carry out the science analysis, and we request the
opportunity to move forward on this significant and exciting program.