WIDE FIELD FIBER-FED OPTICAL MULTI-OBJECT SPECTROMETERWFMOSOPERATIONAL CONCEPT DESIGNDOCUMENT (OCDD)JANUARY 30, 2009

Document Title

WFMOS Initial Operational Concept Design Document

Issue

1.1

Date

January 30, 2009

Contributors to the WFMOS Study:
Richard Ellis, Principal Investigator, Caltech
Mary White, Project Manager, JPL

Chris Evans, UK Astronomy Technology Centre, Royal
Observatory, Edinburgh

Robin Bruno, Proposal Manager, JPL

Beatriz Barbuy, Universidade de Sao Paulo, IAG

Michael Seiffert, Project Scientist, JPL

Luciana Pompeia, IP&D, Universidade do Vale doParaiba

Stuart Lynn, IfA, Univ. of Edinburgh
Peder Norberg, IfA, Univ. of Edinburgh

Dante Minniti, Dept. of Astronomy, Pontificia
Universidad Catolica

John Peacock, IfA, Univ. of EdinburghRich Dekany, Caltech

Fergus Simpson, IfA, Univ. of Edinburgh

Anna Moore, Caltech

Masahiro Takada, IPMU, University of Tokyo

Roger Smith, Caltech

Masami Ouchi, Carnegie Observatories

Steven Beard, UK Astronomy Technology Centre

Thomas Kitching, Univ. of Oxford

Ian Bryson, UK Astronomy Technology Centre

Filipe Abdalla, Univ. College London

Andy Vick, UK Astronomy Technology Centre

Ofer Lehav, Univ. College London

Peter Doel, Univ. College London

Ignacio Ferreras, MSSL—Univ College London
Laerte Sodre, Sau Paulo

Dave King, Univ. of Cambridge

Scott Chapman, Inst. of Astronomy, Univ. ofCambridge

Dave Braun, JPL

Ian Parry, Univ. of Cambridge

Mike Irwin, Inst. of Astronomy, Univ. ofCambridge

Charles Fisher, JPL

Geraint Lewis, School of Physics, Univ. of Sydney

Larry Hovland, JPL

Rodrigo Ibata, Observatoire de Strasbourg

Muthu Jeganathan, JPL

Vanessa Hill, Observatoire de Paris

Joel Kaluzny, JPL

Alan McConnachie, Dept. of Physics andAstronomy, Univ. of Victoria

Roger Lee, JPL

Kim Venn, Dept. of Physics and Astronomy, Univ.
of Victoria

Lewis Roberts, JPLMark Wilkinson, Dept. of Physics & Astronomy,
University of Leicester

Ron Steinkraus, JPL

Amanda Frieze, JPL

Norman Page, JPL
S. Tere Smith, JPL

Nobuo Arimoto, National Astronomical
Observatory of Japan

Karl Reichard, Penn State University

Amina Helmi, Kapteyn Astronomical Institute,
Univ. of Groningen

Ligia Souza de Oliveira, LNA, Brazil

Antonio Cesar de Oliveira, LNA, Brazili

WIDE FIELD FIBER-FED OPTICAL
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Table of Contents
1

INTRODUCTION ................................................................................................................... 1
1.1
Purpose of Document............................................................................................... 1
1.2
Background .............................................................................................................. 1
1.3
Instrument Identification ........................................................................................... 2
1.4
Document Layout ..................................................................................................... 22

DOCUMENTS ....................................................................................................................... 3
2.1
Applicable Documents.............................................................................................. 33

WFMOS, DARK ENERGY, AND MODIFIED GRAVITY........................................................ 4
3.1
The Challenge of Cosmic Acceleration .................................................................... 4
3.1.1 Dark Energy Observables ............................................................................ 4
3.1.2 The Evolution of Dark Energy....................................................................... 6
3.1.3 Modified Gravity............................................................................................ 7
3.2
Baryon Acoustic Oscillations .................................................................................... 9
3.2.1 Standard Rulers............................................................................................ 9
3.2.2 Precision of BAO Measurement ................................................................. 11
3.2.3 Non-linear Effects on BAOs........................................................................ 15
3.2.4 Scale-Dependent Bias................................................................................ 19
3.3
Expected Performance of WFMOS in Measuring the BAO Signature ................... 20
3.3.1 Contrasting High and Low Redshift Surveys .............................................. 22
3.3.2 Fisher Matrices and Figures of Merit .......................................................... 24
3.4
Redshift-Space Distortions and Modified Gravity ................................................... 26
3.5
Further Applications and Challenges ..................................................................... 29
3.5.1 Neutrino Mass Constraints ......................................................................... 29
3.5.2 Galaxy Biasing............................................................................................ 31
3.6
Lensing Synergy..................................................................................................... 34
3.6.1 Strategic Considerations ............................................................................ 34
3.6.2 Benefits of Combining a BAO and 3-D Weak Lensing Survey ................... 34
3.6.3 Broader Synergy between WFMOS and HSC............................................ 37
3.7
Proposed WFMOS Surveys ................................................................................... 38
3.7.1 The International Perspective..................................................................... 38
3.7.2 Low-Redshift Survey .................................................................................. 39
3.7.3 High-Redshift Survey.................................................................................. 44
3.7.4 Requested Time ......................................................................................... 45
3.8
Summary of Survey Strategy for Dark Energy and Modified Gravity ..................... 47

4

STELLAR ARCHAEOLOGY AND GALAXY GENESIS ....................................................... 49
4.1
Science Case ......................................................................................................... 49
4.1.1 Overview..................................................................................................... 49
4.1.2 Redesigning the Galactic Archaeology (GA Surveys) ................................ 50
4.1.3 Addressing the Big Questions .................................................................... 55
4.2
Detailed Survey Design.......................................................................................... 63ii

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4.3

4.4

4.2.1 The Galactic Bulge ..................................................................................... 63
4.2.2 The Outer Disk ........................................................................................... 67
4.2.3 The Galactic Halo ....................................................................................... 68
4.2.4 Targeting Substructure; Streams & Dwarfs ................................................ 71
4.2.5 Targeting M31 & M33 ................................................................................. 71
4.2.6 Overall Survey Design................................................................................ 71
Technical Considerations ....................................................................................... 73
4.3.1 Spectroscopic Requirements...................................................................... 73
4.3.2 Data Reduction Requirements ................................................................... 78
4.3.3 Continuum Estimation ................................................................................ 80
4.3.4 Velocity and EW Estimation ....................................................................... 80
4.3.5 Stellar Atmosphere Modelling..................................................................... 81
4.3.6 Chemical Tagging....................................................................................... 83
Competition ............................................................................................................ 84

5

GALAXY FORMATION AND LARGE SCALE STRUCTURE .............................................. 87
5.1
Introduction............................................................................................................. 87
5.2
Strategic Issues...................................................................................................... 87
5.3
Sample Science Programs..................................................................................... 88
5.3.1 A High Quality Spectroscopic Survey to z ~ 1.6 ......................................... 88
5.3.2 Studies of z > 3 galaxies ............................................................................ 90
5.3.3 QSOs, Clusters, and Other Sources .......................................................... 91
5.3.4 Implications for Instrument Design ............................................................. 92

6

TECHNICAL DETAILS ........................................................................................................ 93
6.1
Introduction............................................................................................................. 93
6.2
Target Catalogs – Dark Energy Surveys ................................................................ 93
6.3
Strategy for Target Selection.................................................................................. 94
6.4
Predicting Emission Lines from Broad-Band Photometry....................................... 96
6.5
Spectroscopic Simulations – Dark Energy Surveys ............................................... 97
6.6
Target Catalogs – Galactic Archaeology................................................................ 99
6.7
Sky Subtraction .................................................................................................... 100
6.8
Fiber Allocation Efficiency .................................................................................... 103
6.9
Fiber Diameter...................................................................................................... 107
6.10
Estimated Survey Time ........................................................................................ 108
6.10.1 Dark Energy.............................................................................................. 108
6.10.2 Galactic Archaeology................................................................................ 110

7

SCIENCE REQUIREMENTS SUMMARY ......................................................................... 111
7.1
Dark Energy Science Objectives and Top Level Requirements........................... 111
7.2
Galactic Archaeology Science Objectives and Top Level Requirements............. 113

8

INSTRUMENT DESCRIPTION.......................................................................................... 115
8.1
System Description .............................................................................................. 115
8.2
Instrument Operational Modes ............................................................................. 119

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8.2.1
8.2.2
8.2.3
8.2.4
8.2.5
9

Start-up..................................................................................................... 119
Target Acquisition..................................................................................... 119
Observation .............................................................................................. 120
Calibration ................................................................................................ 121
Testing...................................................................................................... 122

OPERATIONAL CONCEPT MODEL................................................................................. 123
9.1
Introduction........................................................................................................... 123
9.2
WFMOS Observation Preparation........................................................................ 124
9.3
WFMOS Observation Execution .......................................................................... 125
9.4
WFMOS Engineering ........................................................................................... 127

10 REFERENCES .................................................................................................................. 129
11 ACRONYMS AND ABBREVIATIONS ............................................................................... 133

List of Figures
Figure 3-1: Perturbation around Ωm = 0.25 and w = –1 of the dependence on redshift of the
Hubble parameter H, the distance D and density growth factor g. The parameter p refers toeither w or ln Ωm ; solid lines shows the effect of an increase in w, dashed lines the effect of anincrease in Ωm............................................................................................................................................. 6
Figure 3-2: Acoustic oscillations in the radiation-baryon fluid imprint a pattern in the Fourier
spectrum of both CMB (top) and density (bottom) fluctuations (e.g., Meiksin, White & Peacock1999). In the latter case the effect is much smaller, because the dominant dark matter has no
intrinsic oscillations. Nevertheless, the same acoustic scale can be picked out at low and highredshift, opening the way to a relatively clean geometrical tool in cosmology ............................... 10
Figure 3-3: Confidence contours on the plane of w versus Ωm from the WMAP year-3 data
(Spergel et al. 2006). It is assumed that w does not evolve, and also that the universe isspatially flat, although this latter assumption is not critical. ............................................................... 11
Figure 3-4: Increase in the power spectrum noise as a function of survey galaxy density. For acanonical value of the power spectrum of 2500 (h–1Mpc)3, and assuming more targets thanfibers, a target density of 4 × 10–4 h3Mpc–3 optimizes survey efficiency. At lower densities, shotnoise dominates the error on the power spectrum. Higher target density produces somewhat
improved estimates, but such surveys are less efficient, leading to an overall noise increase if
the survey is constrained to fixed total time. ........................................................................................ 13
Figure 3-5: The matter power spectrum at z = 1 from an N-body simulation of a cube of side2000 h−1 Mpc. Linear theory (green line) is compared with nonlinear results, varying thespectrum normalization (zero-redshift σ8). Note the suppression in power for wavenumbers
beyond the principal peak at k  0.06 hMpc−1, and the visible shift in peak location in the highnormalization case. .................................................................................................................................. 17
Figure 3-6: The fractional shift in the location of the principal acoustic peak owing to large-scalequasilinear effects. These plots consider the mass distribution at z = 1 and varies the zeroredshift normalization, σ8, together with the spectral shape parameter, Ωmh (first panel), and thebaryon fraction, Ωb/Ωm (second panel).................................................................................................. 18
Figure 3-7: The power spectrum from the Millennium Simulation (Springel et al. 2005). The plotshows the linear power spectrum ratioed to a smooth model (black), the nonlinear mass power
spectrum (red), and the galaxies (green), scaled to allow for bias................................................... 20
Figure 3-8: Expected performance of WFMOS for determining DA(z) and H(z) at each redshiftof the surveyed redshift slices based on the BAO geometrical test. The accuracy shown here isrelative to the fiducial model with cosmological constant. We have assumed WFMOS surveys
that differ from the feasibility survey, consistent with our final proposals........................................ 22iv

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Figure 3-9: 68% error on wp, marginalized over other parameter uncertainties, for the model
w(a) = wp+wa(a−ap) with varying parameter ap. The errors are for WFMOS combined with SDSS(z < 0.3) and Planck. The three curves correspond to the results expected when either alone of
the z = 1 or z = 3 slices is used or the two surveys are combined................................................... 24
Figure 3-10: The redshift-space correlation function at 〈z〉 = 0.8, shown in false color toemphasize the Kaiser flattening at amplitudes ξ = 0.1. Note the ‘finger-of-God’ radial stretchingthat arises from virialized random motions at separations of a few Mpc. The superimposed
contours show the best-fitting model of a Kaiser flattening plus this small-scale elongation. The
first panel shows the VVDS measurements with 6000 galaxies (Guzzo et al. 2008); the second
panel is a simulation of the results expected from a forthcoming new VLT survey of 100,000
redshifts. .................................................................................................................................................... 28
Figure 3-11: The masses of the individual neutrino mass eigenstates, as constrained byneutrino oscillation experiments, plotted against the total neutrino mass for a normal hierarchy(solid lines) and an inverted hierarchy (dashed lines). Current cosmological data set an upper
limit on the total mass of light neutrinos of around 0.6 eV ................................................................. 30
Figure 3-12: A potential improvement in the projected 2-parameter 68% C.L. errors in (w0,wa)
subspace, expected for an extensive use of the WFMOS galaxy power spectrum. The
outermost contour shows the forecast when the pure geometrical test of BAO experiment isused. The middle and innermost contours show the improvements when the growth rate
information is included or the redshift distortion is further included. ................................................ 33
Figure 3-13: Dark energy parameters for a WFMOS BAO survey with a HyperSuprimeCam(HSC) 3-D Weak Lensing survey and the Planck Surveyor CMB experiment. From top left toright and top to bottom are shown (i) HSC shear power and Planck CMB, (ii) HSC shear power
and WFMOS BAO, (iii) HSC shear power and shear ratios, (iv) WFMOS BAO and Planck CMB,(v) HSC shear ratios and WFMOS BAO, (vi) shear ratios and Planck CMB. ................................. 35
Figure 3-14: Combining three independent experiments. Left to right: (i) Planck CMB, HSC
shear power and shear ratios, (ii) WFMOS BAO, HSC shear power and shear ratios, (iii)
WFMOS BAO, HSC shear power and Planck CMB, and (iv) WFMOS BAO, HSC shear power
and Planck CMB....................................................................................................................................... 36
Figure 3-15: The redshift distribution of DEEP2 galaxies derived via multicolor selection in BRI
space (Coil et al. 2004). The cutoff at z = 0.7 results from the photometric preselection, whereasthat at z = 1.4 reflects the red limit of DEEP2 spectroscopy. The two lines are alternative models
for the selection function. ........................................................................................................................ 41
Figure 3-16: A plot of the growth factor fg(z) ≡ d ln δ /d ln a against redshift for various models.
The solid black line shows the standard Ωm = 0.25 Λ-dominated model, and the dashed blackline shows the same expansion history but with the stronger DGP-style dependence of growthon Ωm(a): fg(a) = Ωm(a)0.68. The blue dot-dashed line shows the DGP model itself (with Ωm = 0.25
today) and the red treble-dot-dashed line shows the behavior for a standard open Ωm = 0.25
model without dark energy. The three points at z < 1 are (in order of increasing z): 2dFGRS (E.
Hawkins et al., 2003, MNRAS 346, 78); 2SLAQ LRGs (N. Ross et al., 2007, MNRAS 381, 573);
VVDS (Guzzo et al. 2008). The points at higher redshift show the predicted performance fromour proposed WFMOS surveys: 4 × 106 galaxies at redshift z ~ 1 and 105 LBGs at z ~ 3 (eachsplit into two redshift bins). ..................................................................................................................... 44
Figure 3-17: Histograms of the equivalent width of [O II] 3737 emission line derived from
DEEP2 galaxies in two different redshift bins. This indicates that an 80% redshift success rate
will be achieved for the z = 1 survey provided a line of equivalent width as small as 20Å can bereliably detected. ...................................................................................................................................... 46
Figure 4-1: The field of Streams from Belokurov et al. (2006) showing SDSS stars having (g-r)< 0.4 for a region around the North Galactic Cap. Colors denote depth along the line-of-sightwith red being the most distant objects at d~50 kpc and blue the nearest at d~10 kpc. The righthand panel shows a composite including the 2MASS M-giant distribution from Majewski et al.
(2003). Several streams are readily visible.......................................................................................... 50v

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Figure 4-2: The region around the [OI]6300 line from a 5400s VLT-FLAMES exposure at
R=20,000 of a metal-rich, [Fe/H = -0.41 Bulge giant with S:N ~200/A. An unconstrained multicomponent Gaussian model fit of the most prominent lines is shown overlaid in red................... 53
Figure 4-3: The INT/CFHT survey of the southern halo regions of M31; M33 can be seen in thelower left-hand corner of the image. The stellar haloes of M31 & M33 overlap and are repletewith substructure (Ibata et al 2007). ...................................................................................................... 60
Figure 4-4: Dynamical predictions based upon a N-body simulation of a stellar bar and an
axisymmetric bulge model. The comparison assists in determining the observing strategy
necessary to distinguish different models for the Galactic bulge...................................................... 66
Figure 4-5: The properties of a numerical simulation of the demise of the putative Canis Major
dwarf galaxy and the resultant Monoceros Stream (Penarrubia et al. 2005).................................. 67
Figure 4-6: Distribution of debris from an inner halo satellite after 12 Gyr as a function of RA
and Dec. .................................................................................................................................................... 69
Figure 4-7: Aitoff projection of the proposed Primary Galactic Archaeology Survey. .................. 73
Figure 4-8: The curve-of-growth of typical weak lines highlighting the critical dependence of theabundance determination on knowledge of the microturbulent velocity .......................................... 74
Figure 4-9: Top panel: the upper portion presents the CaT region for a K-giant in the Sculptor
dSph with derived [Fe/H] = -1.50 dex LR (-1.63 dex HR), showing an example of automatedcontinuum fitting (dashed line). The lower portion presents the residual after continuum removal
and after subtracting Gaussian model fits to the three CaT lines. Lower panel: the derived crosscorrelation function and associated Gaussian fit around the peak region. ..................................... 75
Figure 4-10: Example of automated continuum estimation and normalization using FLAMES
benchmark HR test data. ........................................................................................................................ 79
Figure 4-11: The best fitting continuum-subtracted model spectra for solar and alpha-enhanced
abundance ratios compared to an HR VLT-FLAMES R=20000 continuum-subtracted spectrumwith S:N ~100/A. Upper panel: continuum-normalized data; lower panel: top is alpha-enhanced
model spectrum (black); middle offset solar ratio model spectrum (green); the mask functionused in the fit is in blue. The solar abundance ratio spectrum gives a marginally better fit. ........ 82
Figure 4-12: Example of chemical tagging for the solar spectrum .................................................. 83
Figure 5-1: Recovering the star formation timescale (τ) and metallicity Z for a red I~22 z~0.7
galaxy. The top panel refers to a low signal/noise spectrum such as that achieved for a darkenergy survey of luminous red galaxies; the data is sufficient to secure the redshift but little
more. The bottom panel refers to a much higher signal/noise spectrum such as would bepossible with longer integrations on brighter objects (R ~ 21) over a wide area. In conjunction
with an earlier survey by FMOS, it is proposed WFMOS could secure robust stellar properties
for a large sample of bright galaxies over 0.7<z<1.6. ........................................................................ 89
Figure 5-2: The spatial distribution Lyman alpha emitters in the well-studied SSA22 field(preliminary results from the ongoing survey, courtesy of Yamada et al). WFMOS, working inconcert with HSC, would enable a huge increase in survey mapping speed and provide
emission line statistics for yet larger samples. .................................................................................... 90
Figure 5-3: Stacked Lyman alpha line profiles for emitters located from Suprime-Cam narrow
band imaging (from the ongoing survey by Yamada and co-workers). Line widths and profile
shapes constrain outflow models and the nature of the local intergalactic medium...................... 91
Figure 5-4: Panoramic spectroscopy of clusters of galaxies at intermediate redshift (courtesy of
Tanaka & Kodama and collaborators). The turn-around region for galaxies falling into a richz~0.5 cluster corresponds to a diameter of ~15–20 Mpc and fits neatly within a single WFMOS
pointing. Detailed spectroscopy provides velocities and star formation diagnostics for thousandsof members per cluster. This will enable a variety of studies which attempt to understand how
the cluster environment both enhances and truncates star formation and rearranges the galaxy
morphologies. ........................................................................................................................................... 92
Figure 6-1: Sky coverage for DES (blue, 5000 deg2 ), KIDS (red, 1500 deg2) and CFHTLS-Wide(green, 150 deg2). Pan-STARRS (omitted for clarity) will map 3π steradians seen from Hawaii.
HSC coverage is not yet decided. ......................................................................................................... 93vi

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Figure 6-2: (Left): Photometric selection from the DEEP2 survey: red and blue points represent
galaxies above and below a redshift of 0.7 respectively, based on BRI photometry alone.
(Right): Simulation illustrating substantially improved photometric redshifts expected for the
Dark Energy Survey (grizY filters) combined with the VISTA Hemisphere Survey (JHK filters).
Deeper data such as that possible with HyperSuprimeCam would further improve the accuracy.
.................................................................................................................................................................... 95
Figure 6-3: Neural network prediction of the [OII] equivalent width given BRI data used to preselect DEEP2 galaxies. With more bands it will be possible to improve this performance. ......... 97
Figure 6-4: (Left) Simulation of WFMOS spectra using realistic sky subtracted noise. We show
as an example a galaxy at the high redshift end of the range for the z~1 DE survey. (Right)
Illustration of the significance needed in order to confirm that the line is indeed [OII]. ................. 98
Figure 6-5: Simulations of high redshift Lyman break galaxies in two different cases: via
absorption lines (top) and via Lyman alpha emission (bottom). The absorption line detection
requires a continuum signal to noise of four and the emission line detection requires acontinuum signal to noise of one. .......................................................................................................... 99
Figure 6-6: Example of automated LR sky subtraction from FLAMES benchmark test data.... 102
Figure 6-7: An illustration of sky subtraction on a SDSS galaxy spectrum. The top panel is theobserved spectrum from the SDSS archive, while the bottom panel is the ‘clean’ spectrum after
sky subtraction with the PCA method. The second and third plots from the top show the ‘sky’
pixels and the reconstructed sky spectrum, respectively (from Wild & Hewett 2005)................. 103
Figure 6-9: Positioner allocation efficiency for the high-resolution spectrograph mode versus
number of targets in the field. Shown are the efficiencies for ideal allocation (black), such as one
might expect from a plug-plate allocation scheme, and the WFMOS high resolution
spectrograph mode (green). The WFMOS efficiency is superior to that achieved by a systemthat selects a fixed subset of fibers to route to the high resolution spectrograph (in blue), as
assumed in the FS. Shown in red is the fraction of astronomical targets successfully assigned toa fiber. ...................................................................................................................................................... 106
Figure 6-10: Signal to noise ratio (S/N) relative to peak S/N as function of fiber angular
diameter, as measured in 0.72 arc sec seeing (i.e., close to the expected median seeing) withexisting Subaru imaging data. Red points show curve-of-growth of flux for stellar objects. Green
data points are S/N vs fiber diameter for bright stellar objects (i.e., negligible sky background),while black data points are for objects with similar mag to sky brightness per sq arcsec. Blue
points show S/N for objects 3–4 mags fainter than sky. .................................................................. 107
Figure 6-11: The red curve shows a histogram of the completeness of our survey fields. We
have assumed that 80% of the fields are observed once (which gives the leftmost bump in the
red curve), and 20% of the fields with poor initial completeness are reobserved, yielding higher
completeness (rightmost bump in red curve). Shown in blue is the tail of the distribution thatwould have remained if these 20% were not re-observed............................................................... 109
Figure 9-1: Use Case Diagrams Key ................................................................................................. 123
Figure 9-2: Use Cases for WFMOS Observation Preparation ....................................................... 124
Figure 9-3: Use Cases for WFMOS Observation Execution .......................................................... 126
Figure 9-4: Use Cases for WFMOS Engineering ............................................................................. 127

List of Tables
Table 3-1: DEEP2 Galaxy Number Counts ......................................................................................... 42
Table 4-1: FLAMES-GIRAFFE R=20000 EWs compared to UVES R=50000............................... 54
Table 4-2: Summary of the Galactic Archaeology Requirements .................................................... 55
Table 4-3: Summary of results of our analysis for block surveys..................................................... 70
Table 4-4: Galactic Archaeology Spectroscopic Requirements Summary..................................... 78
Table 5-1: The Range of Survey Areas and Potential Applications................................................. 88
Table 6-1: Planned imaging surveys: AB magnitude limits per SDSS-like filter. Depths are takenfrom various survey websites. HSC depths follow a private communication (Takada)................. 94

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Table 6-2. Estimated V-band stellar density (per square degree) as a function of galactic
latitude and magnitude limit.................................................................................................................. 106
Table 6-3: Number of fields required for low redshift dark energy survey. We adopt a two-tierstrategy where most (80%) of fields are observed once. The 20% of fields with the worst
completeness are re-observed, yielding higher average completeness with minimal additional
survey time. ............................................................................................................................................. 108
Table 6-4: Required density of galaxies for the low redshift dark energy survey. The required
surface density of successful redshifts is 1245/deg2. To achieve this density, we must start witha catalog of galaxies that reaches RAB = 23.0. .................................................................................. 108
Table 6-5: Survey time requirements for the dark energy surveys................................................ 109
Table 6-6: Number of required fields for the high redshift dark energy survey............................ 109
Table 6-7: Galactic Archaeology survey time requirements. .......................................................... 110

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Organization of Documents
The Study Summary summarizes the concept study and includes an overview of the scientificmotivation and survey strategy (section 2), an instrument overview (sections 3–5), operations description(section 6), and the management and system engineering approach including total cost (section 7). The
Management and Cost Volume provides project organization, cost and schedule estimates, management
techniques, and contract structure. The Detailed Technical Design document is a detailed description of
the instrument that supplements the instrument description in the Study Summary. The Operation
Concept and Design Document presents detailed scientific context, science requirements, andoperational mode and the Level 1 requirements. The Functional/Performance Requirements Document
lists the instrument Level 2 and Level 3 requirements.
The following diagram shows an outline of the document tree with the current document shaded inblue.
Exec. Summary

Study
Summary

Mangmnt
and Cost
VolumeManagement.
Management proposal
including risk
management andsystem engineering
approach.

Detailed
Technical
DesignOCDD

Technical. Full details
on each component ofour design study

Initial Operational
Concept Design
Document – Science
Context, Science
Requirements,
Operational Modes

Cost. Details of
proposal cost
including basis ofestimate andassumptions

ix

FPRD

Functional
Performance
Requirements
Document –Summary Instrument
Description,
Instrument Level
Requirements

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1

INTRODUCTION

1.1

Purpose of Document
The purpose of the Operational Concept Design Document (OCDD) is to provide a vital insight into

how the Wide Field Fiber-Fed Multi-Object Spectrometer (WFMOS) will be deployed and used. The
document has two aims:
1. The scientific motivation and context leading to a full statement of the science requirements
2. A description of the instrument and its operational modes
The first aim of the document will be used as a key input into the derivation of sub-system
requirements and any subsequent design trade-offs. The second will evolve during the concept design andimplementation phases to finally become the instrument’s Operational Manual. The full complement of
instrument requirements as derived from the science requirements and other programmatic requirements
will not be described here, but is documented in the associated Functional Performance Requirements
Document (FPRD). Although it is undesirable to repeat information contained in both documents, forclarity the science requirements are summarized in Section 7 followed by a description of the initial
instrument concept design.

1.2

Background
The Gemini Observatory initiated the “Aspen Process” to define science missions for their second-

generation instrumentation. Through that process, the initial requirements for a Wide-Field Multi-Object
Spectrometer (WFMOS) were identified. In the period to 2005, a consortium led by the Anglo-Australian
Observatory prepared a Feasibility Study (FS) of such an instrument. Gemini, Subaru, and the Gemini
board concluded that WFMOS should be installed on the Subaru telescope where it could share the optics
destined for the proposed prime focus Camera, HyperSuprimeCam (HSC). The Gemini Project’s
Instrument procurement process stipulates the preference for a competitive Conceptual Design Study toconfirm the technical and financial viability of the chosen Instrument and to allow the project to choose ateam to complete the Design and Build Phase.
WFMOS represents an ambitious venture for the Gemini partners. The design and eventual
construction of this instrument is motivated by the need to address several outstanding questions incosmology and Galactic history through comprehensive spectroscopic surveys of galaxies in various
redshift ranges and stellar abundance and dynamical studies in the Galactic disk and halo.
The rationale for an in-depth independent study of WFMOS is manifold. Foremost, the scope of theextragalactic and Galactic surveys necessary to deliver breakthrough science results needs serious
updating and rebalancing. For the cosmological applications, the situation has developed significantly
since 2005. A number of new experiments are now funded or proposed for the study of dark energy and itis critical to better understand the unique role that WFMOS might play. The science return in the Galactic

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component needs to be more quantitatively developed in the context of surveys with a broader range ofoptions. Conceivably, more modest, strategically engineered surveys can be as effective as the very
ambitious ones proposed in the Feasibility Study. Additionally, the survey design should take into
account constraints provided from other facilities, particularly those defining the HSC imaging campaignthat can generate spectroscopic targets and utilize weak gravitational lensing in a complementary mannerto constrain dark energy. All of the above leads to a new set of science requirements with consequent
impact on the instrument design and operation.
Although the Aspen process and the subsequent WFMOS Feasibility Study focused on implementing
two large surveys (dark energy and galactic archaeology), it is clear that a panoramic multi-object
spectrograph such as WFMOS also has the potential for significant discoveries over a much broader areaof astronomy. Earlier equivalent survey spectrographs, the 2-degree field facility and the Sloan Digital
Sky Survey, have readily demonstrated that such carefully planned facilities have an impact far beyond
the science goals which originally motivated their funding and construction.
Our team’s participation in two successive conferences involving substantial attendance from theJapanese community—Panoramic Views of Galaxy Formation and Evolution (Hayama, Japan, December2007) and Cosmology Near and Far – Science with WFMOS (Kona, HI, May 2008)—has brought thispoint into sharp focus. Japanese astronomers (and Japanese members of our team) see WFMOS as a
facility whose impact is likely to extend well beyond that originally envisaged by the Aspen vision.
Accordingly, we have gathered Japanese input on this topic and herein examine, to the extent possible,
whether our design (driven solely by the cosmological and Galactic surveys) is compliant with otherapplications of interest to the Japanese community. This will, we believe, lead to an improvement inJapanese support for WFMOS on Subaru.

1.3

Instrument Identification
The instrument described in this OCDD shall be identified as WFMOS, a Wide-Field, Fiber-Fed

Multi-Object Spectrograph designed to allow both Dark Energy and Galactic Archaeology observations
as identified and described in the WFMOS Science Case.

1.4

Document Layout
In this section we define the purpose of the OCDD, provide some background information about the

project and identify the product. The detailed science cases with justify the surveys are presented in
Sections 3, 4, and 5. Although these are self-contained proposals, more detailed technical descriptions of
the associated target imaging, exposure times, fiber assignment efficiencies and data extraction arecollated in Section 6. Section 7 summarizes the requirements, Section 8 gives an overview of the
instrument by subsystem, and Section 9 describes the use cases and modes of the instrument. The primary
operational scenarios are identified and detailed with an emphasis on how WFMOS interfaces with the
Subaru telescope.

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2

DOCUMENTS

2.1

Applicable Documents

Document Title
WFMOS Feasibility Study Report
WFMOS Feasibility Study Operational Concept Definition Document
WFMOS Conceptual Design Study RFP
Draft Initial OCDD

3

Issue
1.0
1.0
1.0

Date
07/03/05
06/24/05
2007 May 08
2008 Sept 19

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WFMOS, DARK ENERGY, AND MODIFIED GRAVITY

3.1

The Challenge of Cosmic Acceleration
The most radical conclusion of cosmological research in the late 20th Century was that the expansionof the universe has not decelerated as expected from the gravitational effect of the matter it contains. The
most direct evidence for this statement emerged from studies of SNe Ia treated as standard candles (Riess
et al. 1998; Perlmutter et al. 1999), although studies of standard rulers in structure formation had yielded
the same conclusion somewhat earlier (Efstathiou, Sutherland & Maddox 1990). The initial reaction tothese observations has been to assume that the universe contains a cosmological constant, Λ , and this is anatural path to follow given that Λ has been a potential feature of cosmological models from the start ofthe subject. With this addition, the Λ plus cold dark matter ( Λ CDM) model of structure formationaccounts extremely well for a range of data, and has survived the astonishing advances in Cosmic
Microwave Background (CMB) data from the Wilkinson Microwave Anisotropy Probe (WMAP) andother experiments (e.g., Spergel et al. 2003; 2006; Komatsu et al. 2008).
Nevertheless, this is not a satisfactory state of affairs. Following Zeldovich, modern practice is toplace the term Λ on the right hand side of Einstein’s field equations and to interpret it as the energy
density of the vacuum. Discovering the physical origin of such a density is challenging because the value
is extremely small compared to any natural particle physics scale. As a result, the term ‘dark energy’ isoften used to encapsulate our ignorance of the detailed physics that is being probed.
Alternatively, it is possible that the phenomenon of dark energy is entirely illusory. The necessity forthis constituent arises from using the Friedmann equation to describe the evolution of the cosmic
expansion; if this equation is incorrect, it would require the replacement of Einstein’s relativistic theory ofgravity with some new alternative. Either of these alternatives offers an exciting door to new possibilities
in physics, and current cosmological research is absorbed with exploring techniques that can play adecisive role.

3.1.1 Dark Energy Observables
Dark Energy can differ from a classical cosmological constant in being a dynamical phenomenon.
Empirically, this means that it is endowed with two thermodynamic properties that astronomers can try tomeasure: the bulk equation of state and the sound speed. If the sound speed is close to the speed of light,
the effect of this property is confined to very large scales, and mainly manifests itself in the large-angle
multipoles of the CMB anisotropies (DeDeo, Caldwell & Steinhardt 2003). The equation of state,
however, is more readily probed. This is quantified via the equation of state parameter w ≡ P / ρ c 2 ,which can in principle be an evolving function of scale factor, w( a ) .

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For adiabatic expansion, we should in general regard −3( w + 1) as giving the logarithmic rate ofchange of the dark-energy density, d ln ρ / d ln a , so the Friedmann equation gives the epoch-dependent
Hubble parameter as
⎡
⎣

⎤
⎦

H 2 (a) = H 02 ⎢ Ωv e ∫ −3( w( a )+1) d ln a + Ω m a −3 + Ω r a −4 − (Ω − 1)a −2 ⎥ ,(1)where a = 1 / (1 + z ) is the dimensionless scale factor. This change in expansion rate is observable in two
ways: via the geometry of the universe and via the growth of density perturbations.
The comoving distance-redshift relation is one of the chief diagnostics of w. The general definition of
comoving distance D(z) isD( z ) = R0 Sk (r ) , where R0 dr / dz = c / H ( z ) and R0 =|1 − Ωtotal |−1/2 c / H 0 ,(2)and the function Sk(r) is sin or sinh depending on whether the universe is positively or negatively curved.
Any standard length in the universe, such as those defined by the pattern of large-scale structure, thus
subtends an angle involving D(z) if seen transverse to the line of sight, or a redshift increment involving
H(z) if seen radially. In detail, these aspects can be separated, although it is frequently convenient tointroduce DV ( z ) = [ D ( z ) 2 (c / H ( z ))]1/3 (Eisenstein et al. 2005) to approximate what is measured if wehave datasets that average over radial and transverse orientations.
Density fluctuations δ = δρ / ρ (denoting the fractional density perturbation) grow as a function ofscale factor as δ (a) ∝ a at early times, while the universe is matter dominated and curvature can beneglected. But when dark energy becomes important at late times, this growth is reduced, in a way that
depends on the amount of dark energy, and also on its equation of state. We incorporate this into a growth
suppression factor, δ (a) ∝ g (a )a , where g(a) carries the information about the dark sector. As we willshow, g(a) can be probed in a number of ways, either by estimating the mass fluctuations directly, or bymeasuring the peculiar velocities associated with their growth. The principal tools for achieving this aregalaxy clustering and 3-D Weak Lensing; both of these are fields where data from WFMOS can have amajor impact.
The three key quantities H, D & g are all sensitive to dark energy, but how strongly? Figure 3-1
shows what happens when we perturb these quantities about a fiducial Ω m = 0.25 , w = −1 model. In
general, the observables respond rather slowly to changes in w. showing a sensitivity multiplier of about 5(e.g., Peacock et al. 2006), so that a measurement of w to 1% precision requires knowing D to 0.2%
Furthermore, there is substantial degeneracy with Ω m , so that the matter density has to be known well inorder to detect the redshift-dependent effect of a change in w on any of the observables. But the goodnews is that the degree of degeneracy is different for different observables, especially the growth rate.
There is therefore a good prospect for attaining a robust measurement of the equation of state of darkenergy if all the observable signatures can be accessed.

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Figure 3-1: Perturbation around Ωm = 0.25 and w = –1 of the dependence on redshift of the Hubble
parameter H, the distance D and density growth factor g. The parameter p refers to either w or ln Ωm ;solid lines shows the effect of an increase in w, dashed lines the effect of an increase in Ωm.

3.1.2 The Evolution of Dark Energy
Early studies of dark energy tended to treat w as a constant, but physical models for dark energy tend
to be dynamical, so the equation of state is almost certain to evolve. Given the lack of a unique model, thesimplest non-trivial parameterization is (Chevallier & Polarski 2001; Linder 2003);

w(a) = w0 + wa (1 − a) .

(3)

When the evolving w(a ) = w0 + wa (1 − a ) model is matched to data, typically a strong correlation isseen between the inferred values of w0 and wa . This correlation is readily understandable: the bulk of
the sensitivity comes from data at non-zero redshifts, so the z = 0 value is an unobserved extrapolation. It
is better to assume that we are observing the value at some intermediate pivot redshift:w(a) = wpivot + wa (apivot − a ) .

(4)

The pivot redshift is defined so that wpivot and wa are uncorrelated; in effect rotating the goodness of fit
contours on the w0 − wa plane. If we do not want to assume the linear model for w(a), a more generalapproach is given by Simpson & Bridle (2006), who express the effective value of w (treated as constant)as an average over its redshift dependence, with some redshift-dependent weight. Both these weights and6

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the simple pivot redshifts depend on the choice of some fiducial model. With reasonable justification(both from existing data, and also because it is the fiducial model that we seek to disprove), this is
generally taken to be the cosmological constant case.
As will be seen below, the pivot redshift for most experiments tends to be close to z = 0.6, reflecting
the fact that (a) w manifests itself in an integrated signal that builds up from z = 0; (b) if dark energy isclose to Λ in its behavior, the contribution becomes subdominant and hard to measure for z ≥ 1 . Thus, afirst order view of the situation is that different experiments will measure w(z = 0.6) and an uncorrelated
estimate of how rapidly it evolves at that time, wa . The Albrecht et al. (2006) US Dark Energy Task
Force (DETF) advocated a figure of merit, which is the reciprocal product of these two uncertainties, i.e.,
reciprocal of the area of the error ellipse in the w0 − wa plane. However, it is not clear that this is the best
choice: as long as Λ matches the data, the initial challenge is to rule out this w = –1 model, so there could
be a case for trying to optimize the accuracy of measuring wpivot, independent of evolution. But one could
also envisage models that have wpivot = −1 and yet show wa ≠ 0 ; it really depends on one’s prejudice on
how realistic models might occupy the w0 − wa plane. In practice, the axial ratio of the confidence ellipse
is large, so that errors in wa are around 10 times those in wpivot. Thus, if plausible models occupy a small
region near ( w0 , wa ) = (–1, 0), they are much more likely to be detected via their effect on wpivot than via

wa .
But adopting the assumption that Λ will eventually be rejected, measuring evolution would then be
the next goal. For this reason, steps have already been taken to go beyond the simple figure of merit, andthe Figure of Merit Science Working Group (FoMSWG) has produced a more elaborate proposal(Albrecht et al. 2009). This concentrates on how many independent modes in w(a) a given experiment canmeasure, and is certainly an attractive approach once we are sure that a cosmological constant is rejected.

3.1.3 Modified Gravity
Current inferences concerning the presence of dark energy rest on the assumption of the Friedmannequation, based on Einstein’s gravitational field equations. Independent of this, there has been growing
emphasis in the community on the idea of testing Einstein’s General Relativity. In fact, we have little idea
how to approach cosmology without retaining much of the structure of the standard relativistic treatment.
Thus, there is still a metric, within which scalar-mode perturbations can be described in terms of two
potentials:dτ 2 = (1 + Φ)2 dt 2 + (1 − Ψ )2 dσ 2(5)Here, τ is proper time, t is cosmological time and dσ 2 is the spatial part of the metric; this must still
be of the standard Robertson–Walker form, since this is dictated only by symmetry. Observationalcosmology survives unchanged, since photon trajectories are still null. The redshift is still such that 1 + z

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gives the factor by which the universe has expanded since the light we now see was emitted, and thenormal relation between comoving distance and redshift still applies: D ( z ) = ∫ cdz / H ( z )This exact degeneracy between dark energy and modified gravity is broken when we consider aperturbed universe and look at the relation between matter and the potentials. This is simplified in
Einstein gravity because the potentials Φ and Ψ are both equal to the Newtonian potential, which satisfies
the Poisson equation (when nonrelativistic matter dominates). As a result, the growth law for fractional
density fluctuations is rather simple, and can be represented accurately by Peebles’ approximation for the
0.55
logarithmic growth rate: f g ( a ) ≡ d ln δ / d ln a  Ω m ( a ). Linder (2005) suggested that a similar
representation might capture the main behavior in modified gravity models:f g (a ) ≡d ln δ
 Ω m ( a)γ .
d ln a(6)

In Einstein gravity, the approximation γ  0.55 + 0.05[1 + w( z = 1)] works very well, so the growth rate isroughly universal for a given expansion history in conventional gravity theories. But for modified gravity,
this need not be so: the number γ emerges as an important additional parameter in testing the consistencyof the standard framework.
Within this framework, the freedom to depart from Einstein gravity lies in the relation between matterand spacetime curvature. This is described by the field equations that result from adding a gravitational
action to the matter action, and the Einstein–Hilbert action is as simple as possible: proportional to the
Ricci scalar R. A natural line of attack is then to replace this by something more complicated: e.g., R → R+ f(R), where f(R) is some function that becomes negligible at large R so that local tests of gravity are notaffected (Hu & Sawicki 2007). There is enough freedom in this framework to account for any expansion
history of the universe without dark energy, simply by choosing an appropriate f(R). This means that there
is a modified Friedmann equation; it contains familiar elements such as the density parameter, since H(z)and ρ(z) still define a density parameter in the usual way, but now Ω = 1 is no longer required in order for
the universe to be flat.
The most interesting possibilities of this sort to emerge from recent work are modifications motivatedby the predictions from string theory of the existence of higher dimensions. The hidden scale associatedwith these dimensions allows a scale dependence of the strength of gravity, which can mimic cosmic
acceleration. For example, in the DGP model (Dvali, Gabadadze & Porrati 2000), we have the relation
2⎛⎞
1 − Ωm⎛ 1 − Ωm ⎞−3 ⎟+ ⎜H 2 ( z ) = H 02 ⎜
a
+
Ω
m
⎟
⎜ 2
⎟
⎝ 2 ⎠
⎝
⎠

2

(7)

(neglecting radiation), so that the universe tends to a de Sitter model with constant H even without an
explicit vacuum energy. This model provides a direct demonstration that an accelerating universe need

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not require the existence of a physical dark-energy component. The growth of perturbations in the DGP
model can be accurately described by Linder’s model with γ  0.68 ; this is almost 25% larger than thevalue in a Λ -dominated universe, and we show below that relatively modest surveys using WFMOS
would be capable of detecting this difference. The specific DGP model is probably already ruled out
using supernova data (e.g., Sawicki & Carroll 2005; Fairbairn & Goobar 2006); but it has played an
important role in persuading the community that the density growth rate should be treated as a key
cosmological diagnostic.
A key requirement arising from our study is the need for WFMOS to address more advanced models of
this general type. Specifically we wish to both improve our understanding of dark energy along theconventional route of measuring w and its possible evolution, as well as to test whether dark energy
might arise from inadequacies in General Relativity.

3.2
Baryon Acoustic Oscillations
3.2.1 Standard Rulers
The geometrical effect of dark energy can be observed because the pattern of density inhomogeneities
in the universe contains preferred lengths. These are related to the horizon lengths at certain key times,
i.e., to the distance over which causal influences can propagate. There are two main lengths of interest:
the particle horizon at matter-radiation equality (DEQ) and the acoustic horizon at last scattering (DLS). The
former governs the general break scale in the matter power spectrum, and the latter determines the Baryon
Acoustic Oscillations (BAO) by which the power is modulated:
DEQ  123(Ω m h 2 / 0.13) −1 Mpc,
DLS  147 (Ω m h 2 / 0.13) −0.25 (Ωb h 2 / 0.023) −0.08 Mpc.

(8)

Note that the acoustic horizon is smaller than the particle horizon, since it depends on the speed of sound,which is at most cs = c / 3 , and in practice somewhat less owing to the inertia of the baryoniccomponent.These scales can be seen projected on the sky via the CMB power spectrum, and also in thematter power spectrum (see Figure 3-2). Following the important WMAP results, the detailed shape of the
CMB power spectrum breaks the degeneracies implicit in the above scaling formulae, so that theindividual parameters and thus the key horizon lengths are known virtually exactly. Datasets at lower
redshifts probe the same parameters in a different way, which also depends on the assumed value of w:
either directly from D(z) as in SNe, or via the horizon scales as in LSS. Consistency is only obtained for arange of values of w, and the full CMB+LSS+SNe combination already yields impressive accuracy (e.g.,
Spergel et al. 2006):

w = −0.926+−00..051
075

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WIDE FIELD FIBER-FED OPTICAL
MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT(for a spatially flat model). The confidence contours are plotted in detail in Figure 3-3. Any future
experiment must aim for a substantial improvement on this baseline figure.

Figure 3-2: Acoustic oscillations in the radiation-baryon fluid imprint a pattern in the Fourier spectrum ofboth CMB (top) and density (bottom) fluctuations (e.g., Meiksin, White & Peacock 1999). In the latter casethe effect is much smaller, because the dominant dark matter has no intrinsic oscillations. Nevertheless,the same acoustic scale can be picked out at low and high redshift, opening the way to a relatively clean
geometrical tool in cosmology

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Figure 3-3: Confidence contours on the plane of w versus Ωm from the WMAP year-3 data (Spergel et al.
2006). It is assumed that w does not evolve, and also that the universe is spatially flat, although this latterassumption is not critical.

3.2.2 Precision of BAO Measurement
One of the main recommendations of the Feasibility Study was the use of the BAO signature as astandard ruler in order to measure w based on two ambitious surveys undertaken at redshifts z = 1 and z =3. This strategy has already been extensively discussed in the literature (e.g., Blake & Glazebrook 2003;
Glazebrook & Blake 2005; Seo & Eisenstein 2003; 2005; 2007). Here we summarise the main factors thatgovern the precision of such a cosmological experiment.
The limitation on an experiment of this sort is simply the fractional error in the galaxy power
spectrum, P(k), which depends on the number density of galaxies, n, and the range of wavenumber, k, as
follows:

σ ln P =2π⎛ 1 + nP ⎞⎜⎟(Vk 2 Δk )1/ 2 ⎝ nP ⎠(10)

(Feldman, Kaiser & Peacock 1994). Provided nP is greater than of order unity, this represents largely acosmic (or sample)-variance limited measurement. At the wavenumber of the main acoustic feature theobserved galaxy power spectrum displays rather little evolution. We adopt a canonical power value ofP  2500( h −1Mpc)3 at wavenumber k = 0.1h Mpc −1 , motivated by a correlation function scale-length of11

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2.5 h-1 Mpc and slope γ = 1.9 as measured by the VVDS survey (Meneux et al. 2006; Guzzo et al. 2008).
Rather similar parameters apply for z = 3 Lyman-break galaxies (e.g., Hildebrandt et al. 2005).
Assuming that there are more targets than fibers, the volume covered in a fixed time scales as 1/n, sothat the overall power error is minimized at nP = 1 (Kaiser 1986). For the canonical power, this suggests atarget density of n = 4 × 10–4 h3Mpc–3 to nP = 1. This can be reduced somewhat with only moderate
penalty, and an absolute minimum figure is perhaps as low as n = 10–4h3Mpc–3, at which point the error is
25% larger than in the optimal case. Because of the asymmetric nature of the S/N function, only a small
penalty is paid for exceeding the optimum density compared to the relatively abrupt dominance of shotnoise if the density is too low. The noise penalty as a function of galaxy density is shown in Figure 3-4.
As a result of these considerations, we shall generally adopt a slightly more conservatively high target
density:
Maximum target density = 0.001h3 Mpc–3(11)

although bearing in mind that this can be reduced by up to a factor 2 at relatively little cost in S/N. Using
these concepts, we can develop the following useful rule of thumb for the accuracy with which D(z) canbe measured by picking out the acoustic scale:% error in D(z) = (V/5h–3 Gpc)–1/2 (kmax/0.2h Mpc–1)–1/2

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Figure 3-4: Increase in the power spectrum noise as a function of survey galaxy density. For a canonicalvalue of the power spectrum of 2500 (h–1Mpc)3, and assuming more targets than fibers, a target density of
4 × 10–4 h3Mpc–3 optimizes survey efficiency. At lower densities, shot noise dominates the error on the
power spectrum. Higher target density produces somewhat improved estimates, but such surveys areless efficient, leading to an overall noise increase if the survey is constrained to fixed total time.

Here, we have supplemented the obvious scalings with depth and k-space volume with a detailed practical
calculation of magnitude of the precision, as discussed in section 3.3.
We can consider this formula in the context of the two baseline surveys suggested by the Feasibility
Study, at z ~ 1 and z ~ 3 and later we will recommend significantly different survey areas. In practice, ourproposal will increase the redshift bounds of the z ~ 1 survey to exploit new-generation red-sensitiveCCDs:
0.7 < z < 1.6: A1 = 322 deg 2
2.5 < z < 3.3: A1 = 254 deg 2(13)

where A1 is the area required for a 1 (h–1Gpc)3 volume in these redshift bands. Bearing in mind theprecision multiplier factor of 5, we see that a useful advance in precision on w compared to the current
roughly 6% measurement requires a volume in excess of 5 (h–1Gpc)3, or areas of 1610 and 1270 deg2 at

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENTz ~1 and z ~3 respectively. At our minimum density of n = 10–4 h3 Mpc–3, this requires 500,000 galaxies,
rising to 2,000,000 at the optimal density with nP = 1.
One point that requires care is that the expression given earlier for the fractional error on the powerassumes that the galaxy number density is a constant. In reality, no observational technique will assure
this. At z = 1, we will be interested in flux-limited catalogues with depths similar to that of the DEEP2
survey (e.g., Coil et al. 2004), where the number density falls exponentially at high z, declining by at leasta factor of 10 between z = 0.7 and z = 1.6. Similarly, for Lyman-Break galaxies, the standard color
selection is most efficient at z = 3, and the redshift histogram tends to be approximately Gaussian with a
FWHM in redshift of about 0.6. To see how this affects things, consider the full expression for the erroron P(k), assuming that galaxies are given the optimal weight w = 1/(1 + nP) (Feldman, Kaiser & Peacock1994):

σ ln P =(2π )3/ 2 / Vk1/ 2

1/ 2⎛ ( nP )2⎞⎜ (1+ nP )2 dV ⎟
⎝
⎠

∫

,

(14)

where Vk is the volume in k space considered. We have computed this exact expression, setting the mean
number density over the redshift band to be the optimal value, i.e., 〈n〉 = 1/P. In practice, the fractional
power error is within a few % of what would be predicted for a constant density at this level. Therefore,
accurate predictions of surface densities of targets can be made using the simple constant-density error
formula.
One critical issue to be faced when measuring cosmological parameters with the galaxy power
spectrum is whether to use the full information available in the power spectrum, including the horizonscale break, or whether to concentrate on the BAO signal alone. As with current CMB data, use of the full
spectrum places greater weight on the correctness of the CDM model, whereas the relatively compact
BAO signature can be used to pick out as an empirical length scale in a model-independent way (as donein the state-of-the-art BAO analysis of Percival et al. 2007). The full approach can increase the figure of
merit by a factor 3 to 5, but we will generally take the more conservative approach of using only therobust BAO effect.
Finally we mention a recent analysis by MacDonald & Seljak (2008) which shows that the cosmic
variance limit assumed here is not always the ultimate constraint in studies of galaxy clustering, because
there can be multiple galaxy types that trace the underlying density field in different ways. This insightdoes not appear to have any impact on BAO measurement, but it does influence redshift-space distortions,as we discuss below.

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3.2.3 Non-linear Effects on BAOs
As with any high-precision statistical experiment, we need to be sure that the value of small 1/N1/2 is
not dissipated by errors from systematics. Since we aim to locate the BAO scale to a fraction of 1%, weneed to understand distortions in the power spectrum at this level. Such distortions do exist on the scalesof interest. At the simplest level, the matter power spectrum has a non-negligible degree of nonlinearity at
the scale of the principal acoustic peak (k  0.07 h Mpc−1 ) , where the power is suppressed by around 20%.
Such overall matter nonlinearities inevitably lead to shifts in the scale of spectral features. This can beseen intuitively via the Zeldovich approximation, in which we think of the growth of structure not simply
as a linear increase in density, but as an actual collapse that transfers power from one scale to another. In
quantitative detail, this can be simulated readily—at least, in the case of dark matter only. The following
plots (Lynn & Peacock, in preparation) show the effect of varying the main parameters that govern the
power spectrum: the zero-redshift normalization ( σ 8 ), the shape parameter (Ωm h) and the baryon
fraction, (Ωb / Ω m ) . We show both the variation in power spectra in single simulations, and also the shift
in the location of the principal acoustic peak averaged over a number of simulations.
The plots in Figures 3-5 and 3-6 show that the acoustic scale can shift by several percent from its
ideal linear-theory location—and moreover that the amount of the shift depends on the details of themodel. In principle, this is a concern: recall that we need to be able to predict the scale location to a small
fraction of 1%. However, given the increasing precision on the ancillary parameters delivered by WMAP
(and soon Planck), we consider this not to be an issue.
These nonlinear modifications of the intrinsic acoustic scale can be looked at in a different way, as
proposed by Eisenstein et al. (2007). In configuration space, the effect of interest is the alteration of theseparation of pairs of galaxies separated by the acoustic scale of around 150 Mpc, compared to theirlocation according to linear theory. Eisenstein et al. argue that this can be understood as a convolution by
the displacement field. Furthermore, since this is an effect arising from relatively large-scale modes(similar to peculiar velocities), it can at least in part be predicted from the map of the density field andremoved. This ‘acoustic peak reconstruction’ has the potential to make the S/N of the BAO measurement
closer to the simple predictions of linear theory, which are what we have presented in earlier sections.
How much difference this makes depends on how far into the nonlinear regime the analysis is pushed.
This can be controlled more easily in Fourier space, where a number of acoustic harmonics are accessible,
whereas these combine in configuration space to yield a single peak at ~150 Mpc separation in thecorrelation function. At redshifts of a few tenths, the principal acoustic peak at k = 0.06 hMpc−1 is slightly
affected, but the acoustic signal in higher harmonics is largely erased by k = 0.2 hMpc−1 . Eisenstein et al.
(2007) argue that this effect can be largely undone by predicting the displacement field from our map oflarge-scale structure, and correcting the galaxy positions. The claim is that a loss of a factor 4 in S/N cancertainly be reduced to a factor 2, and possibly eliminated altogether.

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Such issues are less important for the WFMOS projects, for two reasons. First, we will work at higher
redshifts than in the study of Eisenstein et al. (2007). The rms displacement field amplitude should scaleaccurately with linear-theory growth, thus being down by a factor 0.75 at z = 1 and 0.56 at z = 3. This
increases the maximum wavenumber at which the acoustic signal survives. Also, as we now explain, awavenumber of k = 0.2 hMpc−1 is in any case dangerously high for other reasons. In short, we do not feelthe successful application of acoustic peak reconstruction is a critical issue.

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Figure 3-5: The matter power spectrum at z = 1 from an N-body simulation of a cube of side 2000 h−1
Mpc. Linear theory (green line) is compared with nonlinear results, varying the spectrum normalization(zero-redshift σ8). Note the suppression in power for wavenumbers beyond the principal peak at
k  0.06 hMpc−1, and the visible shift in peak location in the high-normalization case.

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Figure 3-6: The fractional shift in the location of the principal acoustic peak owing to large-scalequasilinear effects. These plots consider the mass distribution at z = 1 and varies the zero-redshiftnormalization, σ8, together with the spectral shape parameter, Ωmh (first panel), and the baryon fraction,
Ωb/Ωm (second panel).

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3.2.4 Scale-Dependent Bias
The more difficult issue of how galaxies relate to the mass requires detailed simulation, although theresults can be incorporated into the ‘halo model’ formalism (Seljak 2000; Peacock & Smith 2000), andwe intend to use this to perform larger galaxy simulations. In the meantime, we can study the results from
the Millennium Simulation (Springel et al. 2005), which has a simulation volume 500 h–1 on a side. The
semianalytic apparatus yields a galaxy population that should be our best current guide as to what
systematics to expect.
Figure 3-7 contrasts the mass and galaxy power spectra at various epochs. To some extent, this isencouraging for z = 3: the nonlinear mass spectrum clearly shows a third peak at k  0.2 h Mpc–1, which is
not really apparent at either z = 0 or z = 1. On the other hand, the shapes of the galaxy and mass power
spectra start to diverge at smaller k at z = 3 than at z = 1, reflecting the larger degree of bias for galaxies atthat redshift – and indeed the galaxy spectrum shows no more evidence for a third peak at z = 3 than it
does at z = 1. This indicates that one should be extremely cautious about expecting to use galaxy data
beyond the second peak at any redshift. We have therefore generally assumed a conservative limit ofk < 0.15 h Mpc–1 in most analyses.

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Figure 3-7: The power spectrum from the Millennium Simulation (Springel et al. 2005). The plot shows
the linear power spectrum ratioed to a smooth model (black), the nonlinear mass power spectrum (red),and the galaxies (green), scaled to allow for bias.

3.3

Expected Performance of WFMOS in Measuring the BAO Signature
We now look in more detail at the precision we can expect from the BAO test in the context of thesurveys we will justify in more detail in Section 3.7. It is helpful to begin our discussion with forecasting
the expected power of WFMOS to carry out the pure geometrical measurement of H(z) and DA ( z ) ,before relating these to inferences on dark energy. To be more precise, the galaxy power spectrum ismodeled as a set of parameters pα :20

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pα = [Ωm , ωm , ωb , ns , δ R , DA ( z1 ),..., DA ( z N s ), H ( z1 ),..., H ( z N s ), b1 ( z1 ),..., β ( z1 ),...].

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This is not the only possible parameterization. For example, the Hubble parameter does not appear, being
implicit in ωm ≡ Ω m h 2 etc. Similarly, the normalization is given as a curvature fluctuation δ R , rather
than the more down-to-earth σ 8 . These different choices have no significant effect on our conclusions.
The fiducial parameters are chosen to match a flat CDM model with a cosmological constant, consistent
with the WMAP results. Using this parameter set and combined with expected CMB constraints, we canconvert measurement errors on the galaxy power spectrum into errors on the H ( zi ) and DA ( zi )determination at each redshift, marginalized over other parameters (wm, wb, ns, …, β). Hence, ignoring theshape information in the galaxy power spectrum gives us a conservative estimate of the parameter
forecasts. In addition we include only power spectrum information up to some maximum wavenumber
kmax in order to restrict our parameter forecasts to the linear or quasi-nonlinear regime, where kmax foreach redshift slice is set following Seo & Eisenstein (2003).
We apply this method to WFMOS in Figure 3-8. Anticipating the final results of our study, we show
the predictions for the areas we propose in Section 3.7: 3000 deg2 at z~1 and only 100 deg2 at z~3.
WFMOS allows a precise determination of H(z) and DA ( z ) to a few % accuracy for each redshift slice.
In other words, the locations of baryon peaks in P(k) are measured to a precision of 1–2% level for eachslice. Bearing in mind the w multiplier ‘rule of 5’, this shows that such surveys can be expected tomeasure a constant w to a precision of 5–10%. It is also clear from this plot that any systematics leading
to a shift in the baryon peaks needs to be controlled to well under 1% precision in scale in order toachieve the full potential of WFMOS.
As is apparent from the basic P(k) S/N formula, the precision of the BAO experiment depends quite
strongly on the range of wavenumber k considered. Figure 3-8 makes the most optimistic assumption,
which is that a higher redshift slice allows use of the power spectrum to a larger maximum wavenumber

kmax due to the weaker non-linearities in structure formation. In this case, for a given volume and sample
size, H(z) and DA ( z ) are more precisely measured by a z ~3 survey than a z ~ 1 survey. Therefore, if thedark energy density is strongly evolving toward higher redshifts, albeit unlikely, the z ~3 survey could
offer a unique window for probing such a dark energy model. We discuss below whether the assumptionof a wider k range at high redshift is realistic. If it is not, then we should consider the alternative
possibility of a fixed k range – for which the z ~3 experiment is much less impressive, since it inevitably
maps volume at a slower rate than at z ~1. Henceforth, we take this more conservative approach adopting
a maximum k = 0.15 h Mpc–1

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Figure 3-8: Expected performance of WFMOS for determining DA(z) and H(z) at each redshift of thesurveyed redshift slices based on the BAO geometrical test. The accuracy shown here is relative to thefiducial model with cosmological constant. We have assumed WFMOS surveys that differ from thefeasibility survey, consistent with our final proposals.

3.3.1 Contrasting High and Low Redshift Surveys
Given the estimated accuracies of H(z) and DA ( z ) , we can derive the expected performance ofWFMOS for constraining a specific dark energy model. We employ the model in which the evolution ofdark energy as a function of scale factor is specified by the equation of state parameters w0 and wa :w( a ) = w0 + wa (1 − a ) .

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Following the DETF report, we express this in terms of w at the pivot redshift, where the parameters
are then uncorrelated. The error on wpivot tells us that a given dataset can provide the tightest constraint on
the constant equation of state effectively at the pivot redshift zpivot.
Figure 3-9 shows the marginalized error on wp as a function of zp expected from WFMOS combinedwith Planck and SDSS, assuming in the first instance a flat universe, which we emphasize is a significant

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restriction, but useful for a first illustration. The three curves show the results obtained when either one of
the z ~ 1 and z ~ 3 surveys is used or both are combined. As can be seen from each of the curves, the error
on wp is minimized at a particular redshift, the pivot redshift zpivot , showing the best performance of agiven data set to constraint on the constant equation of state parameter, wpivot :z = 1:

zpivot = 0.57, σ ( wpivot ) = 0.068, σ ( wpivot ) × σ ( wa ) = 0.082

z = 3:

zpivot = 0.78, σ ( wpivot ) = 0.110, σ ( wpivot ) × σ ( wa ) = 0.130(17)z = 1 + z = 3: zpivot = 0.64, σ ( wpivot ) = 0.062, σ ( wpivot ) × σ ( wa ) = 0.046
BOSS :
zpivot = 0.52, σ ( wpivot ) = 0.115 σ ( wpivot ) × σ ( wa ) = 0.140
The third quantity σ(wpivot)× σ(wa) gives an estimate of the area of the error ellipse in ( w0 , wa ) plane, thereciprocal of which was advocated by the Dark Energy Task Force as the Figure of Merit (FoM) of agiven data set for constraining the two parameters ( w0 , wa ) simultaneously. Because the dark energy
becomes subdominant beyond z = 1, adding the z = 3 survey improves the accuracy of wpivot by only about
10%. On the other hand, the z = 3 survey modestly improves our ability to constrain a time variation of
the dark energy equation of state: it increases the FoM by about 50% compared to the z = 1 survey alone.
It should be noted that the width of the curves in Figure 3-9 provides a range of redshift where given data
sets have sensitivity to constrain the dark energy equation of state. Adding the z = 3 survey broadens thesensitivity window; for example, for an accuracy of σ ( w0 ) ≤ 0.1 , the z = 1 plus z = 3 surveys yields awide range of the sensitivity redshift, 0.3 ≤ z ≤ 1.

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Figure 3-9: 68% error on wp, marginalized over other parameter uncertainties, for the model w(a) =wp+wa(a−ap) with varying parameter ap. The errors are for WFMOS combined with SDSS (z < 0.3) and
Planck. The three curves correspond to the results expected when either alone of the z = 1 or z = 3 slices
is used or the two surveys are combined.

In practice, the pivot redshifts turn out to be close to redshift 0.6 in most cases. For anything close toa cosmological constant, the vacuum becomes subdominant beyond z = 1. If we had a strong prejudicethat the linear model for the evolution of w(a) was correct, the conclusion from this would be that theextra baseline from z~1 to z~3 adds little to our knowledge of w at the pivot redshift. Since we will seethat z~3 surveys are much more expensive in terms of telescope time, this argues for learning from a welldefined z~1 survey as an initial goal. Of course, we do not have perfect confidence in the linear model, so
z~3 should not be neglected entirely, and in any case we will see that there is a good case from the pointof view of redshift-space distortions for including a modest amount of z~3 data in our main goals.

3.3.2 Fisher Matrices and Figures of Merit
The above discussion has given a simple discussion of the precision that should be attainable with the
BAO method in measuring the acoustic scale using a survey similar to WFMOS. We now go into moredetail on what these measurements would imply for our knowledge of the cosmological model.
The standard methodology for forecasting errors in cosmological parameters is in terms of the Fisher
Matrix, which is the expected curvature matrix of the likelihood of a given model in the face of data:

Fij = − ∂ 2 ln L / ∂pi ∂p j ,

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where the pi are a set of parameters characterizing the model. The inverse of Fij gives a pseudo-covariancematrix, such that the diagonal elements are lower limits to the true variance in a given parameter—
although they are usually taken to be a reasonable estimate of the actual expected error. The Fisher matrix
thus defines a multi-dimensional Gaussian ellipsoid that gives the joint confidence region for theparameters. It is straightforward to marginalize over unwanted parameters (i.e., integrating the likelihood
over all values of the hidden parameters). This yields a lower-dimensional projected likelihood, which in
2D may be plotted as a confidence ellipse, as shown in several plots below. For the present purpose, theplane of interest is wpivot - wa , where these two parameters are uncorrelated, and the DETF Figure of
Merit is constructed in terms of the rms errors on the two parameters:
FoM = [σ ( w pivot )σ ( wa )]−1 ,(19)The Fisher matrix formalism is described in detail in e.g.,the DETF report (Albrecht et al. 2006), andis widely used. It is however surprisingly hard in practice to achieve a set of results that agree well
between different workers. One of the main reasons for this is that Fisher matrices are often illconditioned, with a large dynamic range in their eigenvalues: the very small eigenvalues correspond todirections in parameter space that are poorly constrained. In this case, small numerical errors incalculating the Fisher matrix may result in large changes in the error in those directions. With darkenergy, it is rare for the evolution parameter wa to be accurately determined, so we are always at risk ofencountering this problem. For more detail of how hard it can be to obtain robust BAO figures of merit,
see Rassat et al. (2008).
For this reason, it is helpful to give some explicit examples, based on Fisher matrices provided as partof the DETF work, via the DETFast package. We emphasize that, unlike our simplified example above,
we now allow the curvature of the universe to be a free parameter, consistent with the choice of othercomparable studies. As a baseline, we normally choose to present all accuracy forecasts assuming Planck
data: CMB information constrains many cosmological parameters extremely well, so we have to ask what
the added value of WFMOS will be. But, as shown in Rassat et al. (2008), BAO plus CMB only yields
very weak constraints on dark energy, and it must be supplemented by further information. We take thisto be provided by ‘stage II’ supernova data. Here then are Fisher errors and the FoM for theseassumptions. For BAO data, we consider in turn the DETF ‘stage III’ BAO, which is in effect an earlyversion of WFMOS surveys as envisaged in the feasibility study. To be absolutely explicit, the DETF
Fisher matrix files involved are planckfish.fisher, SN-II.fisher and BAO-IIIs-o.fisher. These all work withan eight-dimensional parameter space, corresponding to a scalar CDM model with arbitrary tilt andarbitrary spatial curvature. We then consider replacing the BAO information with our estimates for
BOSS, WFMOS, and WFMOS+BOSS.

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DETF
BOSS
WFMOS
WFMOS + BOSS

σ ( w pivot )σ ( wa )FoM = [σ ( w pivot )σ ( wa )]−1

0.040
0.041
0.038
0.036

0.447
0.499
0.369
0.334

56
49
71
81

Other calculations are possible, and we emphasize that the exact figure of merit depends on the detailof the information being combined with the BAO experiment, which is why we have been explicit on thispoint. However, our aim here is to demonstrate the increase in power arising from the WFMOS
experiment, which is substantial. We emphasize that this is very much a conservative figure, which could
plausibly be increased to several hundred if it is possible to overcome some of the uncertainties of bias
and use the full power spectrum information.

3.4

Redshift-Space Distortions and Modified Gravity
We now turn to the issue of using redshift-space distortions to go beyond dark-energy models and test

the nature of gravity. The anisotropy of the power spectrum in redshift space derives from the fact that thegrowth of density fluctuations must be accompanied by peculiar velocities, simply from the requirementof mass conservation. At the level of linear perturbations, the comoving peculiar velocity field, u, isrelated to the fractional density perturbation via∇ ⋅ u = −∂δ / ∂t .

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These velocities add to the Hubble motion and cause us to mis-locate particles in the radial direction. This
leads to the characteristic Kaiser (1987) effect, in which the linear mass power spectrum is madeanisotropic:
Ps (k ) = Pr ( k )(1 + f g μ 2 ) 2 ,(21)

where μ is the cosine of the angle between the wavevector and the line of sight and fg is the linear growth
factor introduced earlier: f g = ∂ ln δ / ∂ ln a .
The galaxy power spectrum quantifies how the clustering of galaxies varies as a function of 3dimensional wavenumber, k. However, we cannot measure lengths in real space: rather, we measure (1)angular positions of galaxies on the sky, and (2) radial positions in redshift space. To convert these data topositions in 3-dimensional space, one needs to assume a reference cosmological model, which will differ
from the underlying true cosmology. As a result, the inferred components of the wavenumber along andperpendicular to the line-of-sight are related to the true values via

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H ( z ) refk& ,H ( z)DA ( z )k⊥ ref =k⊥ ,DA ( z ) ref

k&ref =

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where the quantities with and without the subscript ‘ref’ denote the quantities in the reference and truecosmological models, respectively.
An incorrect mapping of the observed angular and redshift positions to 3-dimensional positions
introduces a distortion in the measured power spectrum, known as the ‘geometrical distortion’ (Alcock &Paczynski 1979; Ballinger, Peacock & Heavens 1996; Matsubara & Suto 1996). Taking into account thegeometrical distortion, redshift distortion (Kaiser 1987), and galaxy bias, the apparent galaxy power
spectrum at redshift slice is related to the true linear mass power spectrum P(k, z) as follows:2

k&2 ⎤ 2D ( z )2 H ( z ) ⎡⎢1 + β ( z )⎥ b1 P ( k ; z ),Ps ( k& ref , k⊥ ref ; z ) = A ref
D A ( z ) 2 H ( z ) ref ⎢⎣k⊥2 + k&2 ⎥⎦(23)

where k ≡ (k&2 + k⊥2 )1/ 2 , b1 denotes a linear bias parameter and the linear redshift distortion parameter is

β = (1 / b1 )d ln D / d ln a  Ωm (a)0.6 / b1 .

While the baryon acoustic oscillations, or general features such as the break due to matter-radiation
equality, imprint circular modulated patterns onto the galaxy clustering strength in real spacek ≡ (k⊥ , k& ) , an error in the assumed reference cosmology yields a departure of the rings from perfectcircles. In principle, this allows us to measure both H(z) and DA(z), yielding a double geometrical test of
cosmology. Thus a notable property of the BAO technique is that we can directly measure the Hubble
expansion H(z) at a given redshift, compared to the SNe method that probes the luminosity distance to a
SN at a redshift z, related to the integrated information on H(z) over a range of redshifts[0, z]. One
attraction of this contrast is that SNe measure luminosity distances, whereas BAO measure angulardiameter distances; thus we can test the fundamental prediction of general relativity that DL(z) = (1 + z)2
DA(z).
In practice, it is often convenient to measure redshift-space effects via the correlation function inredshift space ξ (r⊥ , r& ) , where r⊥ and r& are pair separations perpendicular to the line of sight and alongit. In this form, the Kaiser anisotropy tends to produce flattening of the contours of ξ along the line ofsight. Following a number of preliminary attempts, the Kaiser effect was first detected with high S/N by
the 2dFGRS (Peacock et al. 2001), and now the first claims have been made of a high-redshift detection,using the VVDS at z = 0.8 (Guzzo et al. 2008). The VVDS sample uses only 6000 galaxies betweenz = 0.6 and z = 1.2 to IAB < 22.5, and so hardly competes with the 220,000 redshifts of the 2dFGRS, but it
has been accepted as a proof of concept for a larger survey (P.I. Guzzo; provisionally entitled VIPERS:
VImos Public Extragalactic Redshift Survey), which has been allocated 50 VLT nights over the next 3

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years in order to gather 100,000 redshifts using similar techniques to the VVDS. Figure 3-10 contrasts thecurrent VVDS measurement with the predicted result from the new survey.
The complication in interpreting these results comes from galaxy bias. For a highly biased population,
the offsets produced by peculiar velocities are relatively less significant and so the redshift-spacedistortions are less. This is why redshift surveys measure the combination β ≡ fg / b, where b is the linear
bias parameter of the population in question. This number has to be eliminated in order to derive fg itself,
which is the quantity that forms a test of modifications of gravity. Two approaches can be taken to this.
The first is the one used by the 2dFGRS to argue that the absolute degree of bias in that survey was small(Verde et al. 2002): this used higher-order correlations (in practice, the bispectrum—which is the Fourierequivalent of the three-point correlation function). A simpler alternative is to measure the bias directly:
the amplitude of galaxy clustering is observed, and the amplitude of mass fluctuations at intermediateredshifts can be predicted almost exactly using CMB and other constraints, so the ratio of galaxy andmass power spectra can be measured. Of course, this requires us to assume the standard Λ CDM model
for structure growth, which is what we want to test. But taking this route provides a consistency test, sincein effect the value of β can be predicted; if the measured value is different, this amounts to a rejection ofEinstein gravity.

Figure 3-10: The redshift-space correlation function at 〈z〉 = 0.8, shown in false color to emphasize theKaiser flattening at amplitudes ξ = 0.1. Note the ‘finger-of-God’ radial stretching that arises from virialized
random motions at separations of a few Mpc. The superimposed contours show the best-fitting model of a
Kaiser flattening plus this small-scale elongation. The first panel shows the VVDS measurements with
6000 galaxies (Guzzo et al. 2008); the second panel is a simulation of the results expected from aforthcoming new VLT survey of 100,000 redshifts.

It is also necessary to worry about nonlinear effects. These are also visible in Figure 3-10 in the formof so-called Fingers of God, the radial stretching at small separations. This arises because the clustering

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pattern is convolved by the distribution of random virialized velocities internal to dark-matter haloes.
Techniques are well developed to model this distortion analytically, and these methods can be refinedusing N-body simulations. In one respect, this distortion is an advantage, since it means that our redshifts
do not need to be of extreme accuracy: the one-galaxy rms line-of-sight velocity dispersion is 100–
200 km/s, and our redshift accuracy only needs to be below about half this figure in order forspectroscopic measurement error to be relatively unimportant.
An attractive aspect of redshift-space distortions is that they yield a local measurement of fg at theredshift of interest. This is cleaner than BAO measurements, where measuring the apparent BAO scale ata given redshift yields D(z); because this is an integral, it is sensitive to the properties of dark energy at all
redshifts up to z. As we have seen, this is why it is difficult to change the pivot redshift of dark energy
very much even by going from a z = 1 survey to one at z = 3. The high-redshift surveys add power only inthe case of models where there is rapid evolution in the properties of dark energy at high redshifts.
Something similar still applies in this case, however. Redshift-space distortions arise from peculiar
velocities, and it would be unphysical to expect these to undergo sudden jumps. The general equation forthe comoving peculiar velocity is∫u(a ) = a −2 ag ( a ) dt ,(24)

where g is the acceleration vector induced by density perturbations. But in this case, the integral over timerefers to redshifts higher then the one where we make our survey. Since density fluctuations are expected
to grow by a factor  2 between z = 3 and z = 1, there is a good degree of independence between the fg
information at these redshifts.
This fundamental cosmology application is very well suited to WFMOS, and offers the chance toachieve substantial gains over any likely competition at relatively modest cost. This is partly because it isa method that is unique to spectroscopic galaxy surveys, and partly for the reasons of redshift
independence mentioned above. From this point of view, testing gravity via redshift-space distortions is atleast as good a scientific fit for WFMOS as the measurement of w(z) via the BAO signature. The
combination is particularly powerful.

3.5

Further Applications and Challenges
We have so far only considered the application to dark-energy cosmology of the detailed

measurements of galaxy clustering that will be forthcoming from WFMOS. But there are other important
aspects that should not be neglected, and we discuss some of these here.

3.5.1 Neutrino Mass Constraints
A particularly exciting possibility is that WFMOS surveys may be used to measure the neutrino mass.
Neutrinos are so far the only form of particle dark matter whose existence is definitely established. Solar,
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zero mass, but their absolute masses are still unknown. The reason for this is that existing ‘neutrino
oscillation’ experiments measure differences in the mass-squared between three neutrino mass states:2

Δ ( m 21 ) = 8.0 × 10−5 eV 2 ,2

Δ ( m32 ) = 2.5 × 10−3 eV 2 .

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where m1, m2 and m3 are the three mass eigenstates. This information does not give the absolute massscale, nor does it tell us whether there is a normal hierarchy with m3 >> m2 >> m1, or an inverted
hierarchy in which states 1 & 2 are a close doublet lying well above state 3. Cosmology can settle boththese issues by measuring the total mass density in neutrinos. The absolute minimum situation is a normal
hierarchy with m1 negligibly small, in which case the mass is dominated by m3, which would then bearound 0.05 eV. The cosmological limits are already within a power of 10 of this interesting point.

Figure 3-11: The masses of the individual neutrino mass eigenstates, as constrained by neutrino
oscillation experiments, plotted against the total neutrino mass for a normal hierarchy (solid lines) and an
inverted hierarchy (dashed lines). Current cosmological data set an upper limit on the total mass of lightneutrinos of around 0.6 eV

Cosmology can provide an upper limit on the sum of neutrino masses (for a review see e.g., Elgaroy& Lahav 2005; Lesgourgues & Pastor 2006). The key point here is that the behaviour of the matter
spectrum P(k,z) with redshift z is sensitive to the total neutrino mass, in a way distinctly different from thecold dark matter component. This is implemented by measuring the suppression of the power spectrum
due to the neutrino free streaming that takes place while the neutrinos are relativistic. The current mass
upper limit, obtained using CMB WMAP data, SN Ia and the BAO from 2dFGRS and SDSS, is mν,
tot < 0.61 eV at 95% CL (Komatsu et al. 2008).

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The challenge now is to bring down reliably the upper limits to the sub-eV level or even detect theneutrino mass. Conversely, if the sum of the neutrino masses were known from particle physics,
cosmological probes could be used to place tighter constraints on cosmological parameters, e.g., darkenergy parameters the amplitude of fluctuations and the primordial spectral index. We also note adegeneracy between the neutrino mass and the dark energy equation of state parameter w (Lesgourgues &Pastor 2006; Kiakotou, Elgaroy & Lahav 2008), so quantifying the neutrino mass is essential for accurate
measurement of the Dark Energy.
WFMOS can play a key role in setting a tight upper limit on the neutrino mass, and possibly detect it
if the true neutrino mass is sufficiently high. Saito, Takada & Taruya (2008) investigated the non-linear
evolution of the density perturbation in the presence of massive neutrinos and pointed out that the matter
power spectra at intermediate redshifts can be a promising tool for probing neutrino masses. They
demonstrated that the non-linear correction would improve the neutrino mass constraint by a factor of two
compared with assuming just linear theory (which under-predicts the suppression). WFMOS would
enable us to control non-linearity more reliably and this in turn would allow us to use additional data at
smaller scales. The latest calculations by our team members forecast a 1-sigma error of 0.097 eV on mν,
tot from WFMOS. This means that if mν, tot = 0.1 in nature, WFMOS results will be just at the point of a

1-sigma signal. On the other hand if the true neutrino mass is very close to zero, then we can obtain an
upper limit of 0.19 eV (95% CL).
One source of uncertainty in constraining neutrino mass from galaxy surveys is galaxy biasing (e.g.,
Elgaroy & Lahav 2005). Fortunately there are several independent ways of controlling this systematiceffect: (i) modelling the biasing via halo model or semi-analytic simulations and marginalising over thebiasing parameters; (ii) estimating the neutrino mass from galaxy power-spectra derived for different
galaxy types and checking for consistency; and (iii) estimating the biasing empirically from weak-lensing
map to be produced e.g., from HSC.
The level of sensitivity for neutrino mass from WFMOS is of much relevance to the direct
measurement of the neutrino mass from laboratory experiments, e.g., the KATRIN tritium beta decay
experiment. Furthermore, the WFMOS measurements can be combined with laboratory experiment toderive more accurate neutrino masses (e.g., Host et al. 2007 who combined KATRIN with the CMB).
To summarize, if the total neutrino mass is 0.1 eV or so, we should actually detect it with WFMOS.
This would be a development of profound importance for fundamental physics.

3.5.2 Galaxy Biasing
An a-priori understanding of bias may seem an unrealistic assumption, but empirical progress ispossible by using redshift-space distortions. Measuring the redshift distortion parameterβ = Ωγm / b1 .

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allows us to measure the galaxy bias b1 because Ωm (a) is well constrained by other information. In turn,a precise determination of b1 permits a direct determination of the matter power spectrum amplitude,
providing vital information on the growth rate when combined with CMB data. Note that the linear
distortion effect above ceases to be accurate enough for the precision of WFMOS even on the large
spatial scales of our interest, and an issue is how a more sophisticated model of the distortion effectincluding the non-linear correction can be developed analytically as well as based on N-body simulations(e.g., Scoccimarro 2004). Depending on the extent to which this problem can be overcome, the following
two cases might be feasible as possible extensions of the WFMOS experiment from the pure geometricaltest.
(1) In addition to the baryon peak locations, the shape information of the galaxy power spectrum inredshift space may be included for the cosmological interpretation, but b1 and β are treated as free(nuisance) parameters, corresponding to the case where systematics in b1 and β cannot be well controlled.
To be more precise, we deal with a set of parameterspα = [Ωm ,ω m ,ωb , ns , δ R , w0 , wa , b1 ( z1 ),..., b1 ( z N s ), β ( z1 ),..., β ( z N s )](27)

in the Fisher matrix analysis. The angular diameter distance and Hubble function in the redshift-space
power spectrum are both specified by cosmological parameters, and therefore the parameter set above ismore restrictive compared to the full set. This corresponds to a case where we include the shape andamplitude of the redshift-space power spectrum in order to constrain cosmological parameters, but weallow the linear bias and redshift distortion parameters, b1 and β, to vary freely in the model fitting.
(2) Being more ambitious, the redshift distortion effect on the galaxy power spectrum can be usedbased on a reliable model, but without assuming that the bias b1 can be measured. The parameter set
becomes a minimal one

pα = [Ωm ,ω m ,ωb , ns , δ R , w0 , wa , b1 ( z1 ),..., b1 ( z N s )].

(28)

Figure 3-12 shows the expected improvement in the projected error ellipses in the ( w0 , wa ) sub-spacewhen the shape of the galaxy power spectrum is included (intermediate contour) and the redshift
distortion effect is further included (innermost contour). It is interesting to find that the areas of error
ellipses significantly shrink when either or both of the power spectrum shape and the redshift distortion
can be used, compared to the conservative estimate of the pure geometrical test. The pivot redshift, theminimum error on the constant equation of state parameter, and the FoM are as follows, for (1)geometrical test only; (2) including growth; (3) including growth plus redshift-space distortions:(1)

zpivot = 0.64, σ ( wpivot ) = 0.062, σ ( wpivot ) × σ ( wa ) = 0.0460(2)

zpivot = 0.36, σ ( wpivot ) = 0.044, σ ( wpivot ) × σ ( wa ) = 0.0130(3)

zpivot = 0.26, σ ( wpivot ) = 0.025, σ ( wpivot ) × σ ( wa ) = 0.0037

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Figure 3-12: A potential improvement in the projected 2-parameter 68% C.L. errors in (w0,wa) subspace,
expected for an extensive use of the WFMOS galaxy power spectrum. The outermost contour shows theforecast when the pure geometrical test of BAO experiment is used. The middle and innermost contours
show the improvements when the growth rate information is included or the redshift distortion is further
included.

Interestingly, σ(wpivot) and the joint constraints on w0 and wa are improved by a factor of 2.5 and 10,
respectively, with the WFMOS survey alone, if correct models for the power spectrum shape and theredshift distortion are available. Also note that the pivot redshift zpivot becomes close to the present day byadding the information on the growth rate, because the sensitivity of growth rate to dark energy is greaterat lower redshifts.
Finally, an aside on the CMB. We have so far combined predicted Planck temperature correlations
with WFMOS data. It is important to ask how the forecasted errors would degrade if the Planck
information were not available. The corresponding error estimates are

Planck : σ ( wpivot ) = 0.062 (GT), σ ( wpivot ) = 0.025 (GT + Growth + β )WMAP : σ ( wpivot ) = 0.083 (GT), σ ( wpivot ) = 0.031 (GT + Growth + β )(30)Thus, reassuringly, WMAP is approaching an adequate level of precision for the present purpose – andwill certainly become closely comparable to Planck if extended to 8 years as planned.

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3.6

Lensing Synergy
A major strategic driver for WFMOS is the fact that a comparably ambitious instrument,

HyperSuprimeCam (HSC), is now under construction which will utilize the same panoramic prime focus
corrector. We now consider the implications of using WFMOS to perform spectroscopy in areas of thesky that will have been previously imaged by HyperSuprimeCam (HSC). The synergy relates to how
HSC can assist WFMOS (in the provision of targets) and how WFMOS can assist HSC (in calibrating
photometric redshifts). The over-arching factor is the promise of HSC in constraining cosmology throughthe application of weak gravitational lensing.
Weak gravitational lensing distorts, or shears, the images of distant galaxies by deflecting theiremitted light as it travels across the Universe. This deflection is due to the gravitational field ofintervening matter. Gravitational lensing probes the intervening mass distribution independent of its
dynamical and thermodynamic state and independently of galaxy bias. The analysis of weak lensing
signals as a function of the photometric redshifts of the background galaxies is termed 3-D weak lensing.
3-D weak lensing is sensitive to both the growth of structure and the geometry of the Universe. Botheffects can be probed by taking examining shear power spectra or correlation functions of distant galaxy
images as a function of redshift. In addition, the geometry of the Universe can be isolated by taking theratio of the image shear behind dark matter haloes at different redshifts, thereby removing the dependence
on the amplitude and growth of structure.

3.6.1 Strategic Considerations
Conducting a HSC weak lensing survey in the same area as a WFMOS BAO survey would be highly
beneficial. Naturally the magnitude limits are very different; the weak lensing survey must optimize for avery faint limit and high surface density, well beyond reach of WFMOS spectroscopy. Nonetheless, BAO
survey galaxies must be selected from a pre-existing photometric redshift catalogue and a 3-D weaklensing survey requires photometric redshifts that must be extremely accurately calibrated from asubstantial spectroscopic survey. In addition to these technical synergies, the scientific value of
combining 3-D weak lensing with a BAO survey is also substantial, particularly in breaking degeneracies
in the parameters which improve our understanding of dark energy.
When considering dark energy constraints, imaging and spectroscopic surveys involve very different
systematics. BAO surveys suffer from uncertainties arising from galaxy bias, nonlinearity of the galaxy
distribution, and redshift-space distortions. Weak lensing surveys suffer from the need for high image
quality, accurately calibrated photometric redshifts, and uncertainty in the theoretical nonlinear mass
distribution.

3.6.2 Benefits of Combining a BAO and 3-D Weak Lensing Survey
In the following, we quantify the scientific benefits of acquiring weak lensing and WFMOS data inthe same survey areas. We stress that our discussion is schematic as plans for HyperSuprimeCam imaging

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surveys have not yet been agreed. Moreover, as the imaging performance of HSC is not yet verified, we
have not yet incorporated estimates of instrumental systematics in our weak lensing calculations. Suchdetailed calculations are beyond the scope of the current study. As an illustration we assume a common
survey area of 2000 sq deg. for both the imaging and spectroscopic surveys.
Probing dark energy: Figure 3-13 shows the w0 − wa plane for our combined 2000 sq. deg.

WFMOS and HSC surveys where we have assumed the latter achieves a surface density of 30 galaxies
arcmin-2 and median redshift of z = 1. The 3D Weak Lensing survey is used to estimate the 3-D shear
power spectrum and a shear ratio analysis provides a geometrical constraint, in conjunction with Planck.
Assumingan11-parametercosmological

model

with

spatialcurvature,(Ω m , Ωv , Ωb , Ω tot , σ 8 , h, τ , ns , r , w0 , wa ) , we have marginalized over all other parameters. Contours are

two-parameter, one-sigma (68%) confidence contours, while the marginalized one-parameter, one-sigma
error is quoted.

Figure 3-13: Dark energy parameters for a WFMOS BAO survey with a HyperSuprimeCam (HSC) 3-D
Weak Lensing survey and the Planck Surveyor CMB experiment. From top left to right and top to bottom
are shown (i) HSC shear power and Planck CMB, (ii) HSC shear power and WFMOS BAO, (iii) HSC
shear power and shear ratios, (iv) WFMOS BAO and Planck CMB, (v) HSC shear ratios and WFMOS
BAO, (vi) shear ratios and Planck CMB.

Weak Lensing alone: On its own our assumed HSC weak lensing survey, combining dark energy

estimates from shear power and shear ratios, can achieve δ w0 = 0.06 and δ wa = 0.37 .

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Combining two probes: The HSC shear power analysis alone, combined with either Planck or with

the WFMOS BAO survey, will yield around δ w0 = 0.10 and δ wa = 0.25 . The HSC shear-ratio test alone,
combined with Planck, will yield δ w0 = 0.06 and δ wa = 0.27 , while combining it with WFMOS BAO’s
will yield δ w0 = 0.08 and δ wa = 0.29 . This can be compared with the WFMOS BAO experiment, which
can be combined with Planck to yield δ w0 = 0.07 and δ wa = 0.15 . While these estimates of the darkenergy parameters are not strictly independent, since we only have three probes, they can be compared asa check for consistency. Any notable deviation may indicate the presence of an unaccounted systematic,
or effect from modified gravity.
Combining three probes: Figure 3-14 shows the results of combining three of the four experiments:

WFMOS BAO, HSC shear power and shear ratios, and Planck CMB. In all cases, the combinations canyield around δ w0 = 0.035 and δ wa = 0.10 , and again provided a series of consistency checks.
All four probes: Combining all four methods yields an accuracy of δ w0 = 0.027 and δ wa = 0.082
Galaxy bias: As well as directly measuring dark energy properties, weak lensing can also probe thedependence between mass and galaxies. In particular weak lensing can study the possibility of scaledependent galaxy bias on large scales, which could affect the BAO measurement. Since the effects ofgalaxy bias are perhaps the largest uncertainty for the BAO method, a direct test of this with lensing
would significantly improve the fidelity of the BAO results.
3-D mass mapping: With a well-calibrated 3-D photometric redshift weak lensing survey, the3-dimensional mass distribution can be mapped. This can be used to study both the cluster-scale dark
matter haloes, and the evolution of dark matter perturbations with redshift.

Figure 3-14: Combining three independent experiments. Left to right: (i) Planck CMB, HSC shear powerand shear ratios, (ii) WFMOS BAO, HSC shear power and shear ratios, (iii) WFMOS BAO, HSC shear
power and Planck CMB, and (iv) WFMOS BAO, HSC shear power and Planck CMB.

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In summary, by coordinating HSC and WFMOS surveys:

1. The shear power spectra and shear-ratio geometric tests of weak lensing can be used to break thew0 − wa parameter degeneracy present in WFMOS BAO results.
2. We can examine how well light (as delineated by the galaxy distribution) traces dark matter (asrevealed by weak lensing). This can be used to probe systematic effects in BAO by measuringgalaxy bias as a function of scale.
3. Weak lensing can deliver 3-D mass maps and evolution of dark matter perturbations with redshift
up to z = 1.

3.6.3 Broader Synergy between WFMOS and HSC
Weak lensing with HSC will achieve the ultimate cosmological accuracy in the next decade by using
photometric redshifts for a telescope widely considered to offer the best ground-based image quality.
However, this goal will only be achieved if biases in the photo-z’s can be very accurately characterized.
Following Huterer et al. (2006), the mean redshift in each photometric redshift bin needs to be knownwith ~1% accuracy in order to avoid a significant bias in dark energy parameter constraints. The
statistical random errors on photo-z’s are not problematic, because the errors are expected to be typicallyδz ~ 0.1 for individual galaxies and ~108 galaxies are available in each redshift bin to average down thisscatter. The major issue is to remove contamination caused by catastrophic photo-z outliers. A training
spectroscopic galaxy sample is essential. We note that likely HSC surveys will not be shot noise limited,
so that a significant fraction of galaxies can be discarded if their redshifts are ill-defined without severely
degrading parameter accuracy (see e.g.,Jain, Connolly & Takada 2007).
Clearly WFMOS can offer an ideal training spectroscopic dataset. Following Huterer et al. (2006) and
Ma, Hu & Huterer (2006), a spectroscopic sample of ~ 106 galaxies with redshift coverage 0.7<z<1.6 issufficient to reduce the photo-z bias down to δz ~ 10-3.5 , well below the required 1% accuracy. However,this calibration is valid only if the WFMOS spectroscopic galaxies are a representative fair sample of the
HSC weak lensing imaging galaxies. This is unlikely because WFMOS will target particular types ofgalaxies such as emission-line galaxies. Therefore the new method recently proposed by Newman (2008)
will need to be used to further calibrate the residual photo-z errors. Here, the angular cross-correlations
between the HSC photo-z galaxies and WFMOS spectroscopic galaxies as a function of redshift, in
combination with the auto correlation functions, are used to reconstruct the true redshift distribution of thephotometric galaxy sample. Crucially, this test will only be possible if the imaging and spectroscopic
fields overlap. According to Newman (2008), the photo-z bias can be reduced in this way to δz ~ 10-4 over
the redshift range where the WFMOS galaxies overlap with the HSC sample. Provided the photometry
can be obtained in a uniform way, calibration obtained in the WFMOS region can then be extrapolated toa larger area. Thus, the overlapping in survey sky with HSC and WFMOS brings a huge benefit for
improving dark energy constraints and calibrating the photo-z error contamination in HSC and other weaklensing surveys.

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3.7
Proposed WFMOS Surveys
3.7.1 The International Perspective
The outlook for cosmology has naturally been much debated throughout the global community in thesubject. For the present purpose, the USA’s DoE/NASA/NSF interagency task force on Dark Energy(Albrecht et al. 2006), Europe’s ESA-ESO Report on Fundamental Cosmology (Peacock et al. 2006), andalso the UK PPARC Dark-Energy Review (Trotta & Bower 2006) are especially important in setting thecontext. In general, these documents agree on the power of the BAO technique, and on the need to pursue
a number of independent methods.
The Dark Energy Task Force (DETF) takes the standard approach of parameterizing the dark energy
equation of state as w(a ) = w0 + wa (1 − a ) and advocates a figure of merit for any given experiment that is
the reciprocal area of the error ellipse in the w0 + wa plane. This is not an unreasonable choice, but it does
presume that nontrivial DE dynamics will be detected in due course, whereas the greatest immediateadvance one could imagine in the field is to rule out the cosmological constant model. Until or unless thisis achieved, it arguably makes sense to optimize the error on a constant w, independent of evolution. Thisdifference in emphasis does not matter hugely in practice. Albrecht et al. define a number of stages for
DE probes:•

Stage I: Current knowledge.

•

Stage II: Ongoing projects.

•

Stage III: Near-term, medium-cost projects, which should deliver a factor of 3 improvement infigure of merit over stage II.

•

Stage IV: Long-term, high-cost projects, which should deliver a factor of 10 improvement infigure of merit over stage II. These are taken to be LSST, SKA, plus one space mission expected
to emerge from the NASA/DoE JDEM process.

In the DETF lexicon, WFMOS would be a stage III project, in competition with cluster surveys,
supernova surveys and weak lensing surveys. The DETF and ESA-ESO reports argue that all these four
principal techniques should be pursued, since one method in isolation may be limited by unforeseensystematics, thus failing to deliver the required accuracy. We agree with this multi-pronged strategy: thisis one reason why it is attractive to consider both the BAO and redshift-distortion signatures as
independent probes of dark energy that can be derived from redshift surveys carried out with WFMOS.
Considering the years prior to the commissioning of WFMOS, there will be three ground-based
surveys that are capable of making interesting advances in this area:
1. The AAT WiggleZ survey, which aims to measure 140,000 redshifts over 1000 deg2 between
2006 and 2010. Their redshift distribution is sharply peaked around z = 0.6, with a FWHM in
redshift of about 0.4.
2. The VIPERS Survey of 105 redshifts over 0.6 < z < 1.2 between 2009 and 2012. This will use
VIMOS to cover an area of 24 deg2 to IAB < 22.5.

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3. BOSS, which is the SDSS-III campaign to measure 106 LRG redshifts at z < 0.8 over 10,000 deg2
between 2009 and 2014.
The WiggleZ survey is of similar size to the VLT survey, but is more sparsely sampled; it will
therefore not yield a very strong result for redshift-space distortions. Increasing the space density would
push the magnitude limit beyond what is feasible with the 4-m AAOmega instrument. Conversely, the
VLT survey covers too small a volume to be powerful for BAO. Large-area VLT surveys are limited by
the 4×7′×8′ field of VIMOS, so WFMOS would have no competition in high-density surveys to 8-m
depth over areas above 100 deg2. Of the existing ground-based projects, however, BOSS will still be
extremely impressive, despite being performed with a 2.5-m telescope, on account of its huge area; BOSS
will define the landscape for studies of BAO and redshift-space distortions at the time of WFMOS
commissioning. WFMOS should certainly aim for a substantial improvement over what is likely toemerge from BOSS, and this can be done in two ways: size and redshift coverage, as discussed below.
In the longer term, dark energy experiments may be performed from space. Both NASA and ESA are
considering projects that will yield large spectroscopic samples (>108 redshifts). At the time of writing theNASA/DoE Joint Dark Energy Mission will provide relatively low-resolution grism coverage of 20,000
deg2.. Likewise, ESA has under study the ambitious EUCLID project, which aims to achieve near-IR slit
spectra to HAB=23 over 20,000 deg2 between 0.8 and 1.8 microns (1.1 < z < 3.8 for [OII] 3727 Åemission). Most likely the ESA and NASA/DoE missions will merge in which case a 2017 launch isalmost certainly optimistic. We can therefore say with some confidence that a clear competitive niche
exists for spectroscopic dark-energy studies using WFMOS, provided the instrument can be constructedby 2014–2015 and its survey program completed by no later than 2018–2020.

3.7.2 Low-Redshift Survey
SummaryThis survey will measure 4,000,000 galaxies within a redshift range of 0.7 < z < 1.6 over about
3,200 deg2, to a limit of RAB < 23.0. The required time is approximately 832 hours or about 83 clear
nights.
Although the section below can be read as a self-contained proposal, more technical details covering
the necessary imaging for target selection, exposure times, fiber assignment statistics, sky subtraction etc.,
are presented in Section 6.
Performance as a BAO Experiment

The principal driver for the size of this survey is the desire to design an experiment that will consitutea significant advance in terms of BAO measurement on BOSS, which will measure redshifts for≈ 1,000,000 LRGs at z < 0.8 between 2009 and 2014. In the earlier discussion of BAO statistics, we haveadopted a fiducial accuracy of 1% precision in measuring the BAO scale, i.e., an uncertainty of 5% in w

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENTat the pivot redshift. This is the level of accuracy that BOSS is expected to deliver. A WFMOS survey canbe complementary in its redshift coverage, and this motivates us to extend the spectral coverage well into
the red, reaching to z = 1.6 for galaxies where the principal means of detection is via the [OII] 3727 Åemission line. As discussed in §6, color-based selection will enable us to cut the survey off in an efficient
way at z = 0.7, thus probing a redshift band completely independent of BOSS. Because the distanceredshift relation is an integrated quantity, the impact of high-redshift data depends very much on the
speed of evolution in w(z): high-redshift measurements are important if the evolution is strong, butconversely the pivot redshift is not strongly sensitive to the upper redshift probed in cases where the darkenergy is closer to a cosmological constant. A comprehensive WFMOS survey should therefore also belarge enough to constitute a significant advance in the precision with which a constant w could bemeasured, while remaining realistic in terms of its total time requirements. We propose to set the size ofthe z = 1 survey at a total of 4,000,000 galaxies, which would push the precision on constant w down to
3%, This opens the chance of being the first to detect a discrepancy with the basic cosmological constantmodel. The total time requirement of under 100 nights should be attainable as a large Subaru project over
3 years, and is certainly an investment that is justified by the fundamental importance of the problem.
WFMOS is a large and expensive instrument, and a BAO survey will be one of its flagship projects.,
being sure to retain coverage up to z = 1.6 to maximize complementarity with BOSS.
Performance as a Redshift Distortion Experiment

A survey of this size will also take the growth rate of fluctuations, fg at z =1 into a completely new
regime of precision. Present data from the VVDS (Guzzo et al. 2008) barely suffice to show that fg is nonzero, but this is based on only 6,000 redshifts. The next step will be the 100,000 redshifts of the VIPERS
project, which works to the same limit as the VVDS, IAB < 22.5, which is very close to the limit required
for the z =1 WFMOS BAO survey. The high surface density of these fully sampled surveys makes themespecially suitable for measuring redshift-space distortions: here, the clustering anisotropy signal isstrongest at small scales, and is best measured at around 20 Mpc separation, where the results are robustwith respect to nonlinear effects from small-scale peculiar velocities. This contrasts with the 100-Mpc
BAO feature in the correlation function, where a sparse-sampling strategy would be worth considering if
we were only interested in the BAO signature. But in order to make a good job of measuring the two
distinct fundamental-cosmology signatures, the number density should be kept high.
It is also important to achieve a redshift accuracy for which the radial stretching associated withmeasurement errors does not swamp the large-scale cosmological clustering anisotropy. At low redshift,
the pairwise velocity dispersions of galaxies are about 400 kms-1 for blue galaxies, or 600 kms-1 for redgalaxies (Madgwick et al. 2003). These figures are expected theoretically to show only a modest declinewith redshift, and Guzzo et al. (2008) measure about 400 kms-1 at <z> = 0.8. The equivalent 1-galaxy
redshift error is Δz / (1+z) = 0.0007. Ideally, the fractional error with which we can measure thewavelength of emission lines would be smaller than this, but this is not essential. The reason for this is40

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that the physical peculiar velocities cannot be predicted exactly from theory in any case: they have to betreated as a nuisance parameter, to be estimated empirically. If the total effective pairwise dispersion isincreased by redshift errors, then this has little impact on the overall S/N with which fg can be measured,
as long as the change in effective total velocity dispersion is not gross. The empirical accuracy of VVDS
redshifts is an rms Δz = 0.0012 between repeated measurements, and we would expect WFMOS surveys
to do rather better than this, because the spectral resolution will be about 10 times better than that of
VVDS. The rms wavelength error with which it is possible to centroid a spectral line of rms width σ isjust Δλ = 21/2 σ / SNR, thus depending on the signal-to-noise with which the line is detected. This will bea minimum of about 5 for a secure detection, and so the fractional precision in wavelength should be
0.0001 or better, adopting our default resolution of R = 3500. This minimal figure can be increased bycalibration errors, of course, but in practice keeping these to the Δz = 0.001 level should not be difficult.
Overall, then, we can predict the precision in fg that is expected from the WFMOS z = 1 survey
straightforwardly by scaling from the projected performance of VIPERS, which in turn is based on
simulations grounded in the VVDS experience. The error in fg scales as V-0.5 n-0.44 in terms of the survey
volume V and number density n. The VLT work will cover 0.5 < z < 1.2 with 100,000 galaxies over 24
deg2, and is predicted to yield an error of 0.07 in fg. A WFMOS survey of 4,000,000 galaxies covering
0.7 < z < 1.6 should thus improve this by a factor 5, yielding an expected precision on fg of about 0.02 in 2
independent redshift bins around z = 1 (see Figure 3-17).

Figure 3-15: The redshift distribution of DEEP2 galaxies derived via multicolor selection in BRI space(Coil et al. 2004). The cutoff at z = 0.7 results from the photometric preselection, whereas that at z =1.4 reflects the red limit of DEEP2 spectroscopy. The two lines are alternative models for the selection
function.

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Practical Issues of Sample Selection

We begin by recapitulating the arguments that specify the depth of the sample in order to attain goodBAO performance:
1. Volume density. nP = 1 is optimal for power spectrum measurement. We adopt a canonicalP = 2500 h-3 Mpc3 at wavenumber k = 0.1 h Mpc-1, motivated by a correlation function scalelength of 3.5 h-1 Mpc and slope γ = 1.9 as measured for VVDS. Very similar parameters apply forz = 3 Lyman-break galaxies. This specifies a target number density of n = 0.0004 h3Mpc-3. At
face value, a larger figure is required in order to achieve accurate measurement of power at larger
wavenumbers; even though the BAO signal is concentrated at k < 0.1, understanding of nonlinear
systematics will be critical, and we wish to retain the ability to measure the power in this regime
accurately. However, the degradation in performance at a factor 2 below optimum density is
minor, and so we believe that n = 0.0004 h3Mpc-3 is a sensible target.
2. With a default Ωm = 0.25 flat cosmology, this corresponds to a desired surface density ofgalaxies in the range 0.7 < z < 1.6 of 1245 deg-2. We emphasize that this should be the density ofactually achieved redshifts in this range. For a survey size of 4,000,000 galaxies, this corresponds
to a total area of 3200 deg2.
3. Since our simulations (Section 6) show that the best attainable fiber allocation completenessfigures in practice will be about 80%, the surface density of required targets rises accordingly.
We show below that a further 20% of targets will probably have too weak lines to yield a redshift.
Allowing for these two inefficiencies, we require an input surface density of galaxies in the range
0.7 < z < 1.6 of 2132 deg-2.
The z = 1 band is has been thoroughly explored to the depth of interest by the DEEP2 survey, and it
seems sensible to build on the experience of the DEEP2 team with color-based selection of a redshiftband at the depths of interest. As described earlier, the low redshift survey will cover the redshift range of
0.7 < z < 1.6, and the bulk of our survey will consist of emission line galaxies.
Figure 3-16 shows the redshift distribution of DEEP2 galaxies derived to a limit of RAB < 24.1 (Coilet al. 2004). Multicolor selection in BRI space was applied, and a sharp cutoff at z = 0.7 was achieved,
such that about 95% of selected galaxies did in fact exceed that desired threshold. The DEEP2 number
counts are given in Table 3-1 (all mags are AB). We choose to select in R both because this is near thecenter of the spectral range, and also to exploit the DEEP2 experience.
Table 3-1: DEEP2 Galaxy Number Counts
R mag range
Rlim
N/deg-2
N(21 < R < Rlim)
24.0–24.5
24.5
25,70070,70023.5–24.0
24.0
17,00045,00023.0–23.5
23.5
11,20028,00022.5–23.0
23.0
7,100
16,00022.0–22.5
22.5
4,600
9,700
21.5–22.0
22.0
3,100
5,100
21,0–21.5
21.5
2,050
2,050

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The surface density of galaxies that satisfy z > 0.7 according to the DEEP2 color cuts isapproximately 15,000 deg–2 to RAB < 24, declining to about 900 deg-2 for RAB < 22.5. Our desired densityof 2132 deg-2 thus requires a limit of
RAB<23.0(28)

Although we have deduced this figure from first principles, it is rather close to the limit suggested for thez = 1 survey in the feasibility study.
To a given magnitude limit, the majority of the selected galaxies are blue emission-line objects. The
alternative class is the Luminous Red Galaxies (LRGs), but these are much rarer. Again based on the
DEEP2 results, a surface density of LRGs comparable to that of blue galaxies at RAB < 22.7 requires RAB< 23.6. These objects will be more strongly clustered than the blue population, but not by as much as one
might first think. Local studies (e.g., Norberg et al. 2002) indicate that clustering increases both withcolor and with luminosity, and we are already selecting the relatively rare luminous subset of the blue
population. Probably the additional color selection would amount to a further relative bias factor of
approximately 1.5, which does not have a large impact on SNR if we are near nP = 1. As seen below, theextra integration time needed to access the LRG population means that they are not competitive probes.
Over a 1.5-degree field, the range of densities discussed here corresponds to 3400 targettable objects
per field. The number of required positioner elements depends on the number of multiple visits per field.
The most efficient way of covering area is with a single visit, so that more than 3400 would never berequired. For a single visit, the completeness would be lower than the canonical 80%—more like 50%.
But most of the efficiency gains come when going from single to double coverage per pointing. Thus, atminimum there should be sufficient fibers to match the target number in two visits, i.e., 1700. To secure
our final observing requirement we adopt a strategy of re-observing 20% of the fields, selecting thosewith the worst completeness. This boosts the average completeness and has the benefit of allowing
repeated fields to be useful as a check on systematics. To reach our goal of 4 million galaxies, assuming a
80% successful redshift fraction, we need 2380 fields of which 473 are measured twice. Further details
supporting this strategy are presented in Section 6.

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Figure 3-16: A plot of the growth factor fg(z) ≡ d ln δ /d ln a against redshift for various models. The
solid black line shows the standard Ωm = 0.25 Λ-dominated model, and the dashed black line shows
the same expansion history but with the stronger DGP-style dependence of growth on Ωm(a): fg(a) =Ωm(a)0.68. The blue dot-dashed line shows the DGP model itself (with Ωm = 0.25 today) and the redtreble-dot-dashed line shows the behavior for a standard open Ωm = 0.25 model without dark energy.
The three points at z < 1 are (in order of increasing z): 2dFGRS (E. Hawkins et al., 2003, MNRAS 346,
78); 2SLAQ LRGs (N. Ross et al., 2007, MNRAS 381, 573); VVDS (Guzzo et al. 2008). The points at
higher redshift show the predicted performance from our proposed WFMOS surveys: 4 × 106 galaxies
at redshift z ~ 1 and 105 LBGs at z ~ 3 (each split into two redshift bins).

3.7.3 High-Redshift Survey
A high-volume, high-redshift (i.e., z = 3) BAO survey, such as that proposed in the Feasibility Study,
would offer an important complement to the z = 1 survey, improving constraints on evolution of darkenergy to high redshift. We have concluded, however, that because of the observing time expense and forsound strategic reasons related to the unknown nature of dark energy, it is premature to define a required
high-redshift survey. In lieu of this, we will make it a requirement that the instrument be designed to becapable of such a survey. We describe such a survey below. As before, details on target imaging andexposure times are available in Section 6.
A high-redshift survey would use Lyman-break galaxies, chosen using a Steidel-like UGR selection.
In the redshift band 2.5 < z < 3.3, the volume per unit area is somewhat larger than the figure for the lowredshift band, so the optimal target density is approximately 1600 deg–2, or 2800 objects per 1.5-degree
field. This density of targets corresponds to a magnitude limit of RAB < 25. The number of fibers required
follows the same logic as the z = 1 survey: one might contemplate strategies between double visits for a
low density to single visits at the maximum density. This suggests a compromise figure for the total

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number of positioner elements near the z = 3 minimum. A total of 1400 would allow the z = 1 survey to
be executed very well – and we shall argue below that this is probably the more important redshift band.
How large a z = 3 survey would be required? We have been using the current precision on constant w
of 5% as a target, which implies a volume of 5 (h–1 Gpc)3. Thus the natural figure is 2,000,000 galaxies,
or an area of 1570 deg2. Here, the agreement with the Feasibility Study is not so good. Their proposal wasfor a survey of 600,000 galaxies, using a slightly brighter limit: RAB < 24.5. The claim for this survey wasan accuracy of 1.5% in D(z), or 7.5% in w. The difference is that we prefer to make the more conservative
assumption that one cannot work to higher kmax at higher redshift, which is how the Feasibility Study wasable to obtain such a good accuracy for a relatively small sample. The results from the Millennium
Simulation shown above certainly cast doubt on this optimistic approach: although the nonlinearities in
the mass distribution are lower at z = 3, the nonlinearities in the galaxy distribution seem to be higher.
Our main issue with the z = 3 survey proposed in the FS is that it is much more expensive in terms oftime than the z = 1 survey for the same precision on w, and that it does not deliver a large increase inpivot redshift (as discussed earlier). We therefore envisage a staged approach consisting of an initial
redshift-distortion survey of 100,000 galaxies to constrain the growth of structure. Only 35 nights would
be needed for a survey of 100,000 Lyman-break galaxies at z = 3. Although redshifts of galaxies at thisdistance are slow and expensive to obtain relative to the z = 1 band, in terms of redshift space distortions
this will be unexplored territory, and so even 105 galaxies in 35 nights would transform the observationalsituation regarding constraints on fg in this regime. Scaling from the projections of VLT VIPERS project,
this would yield an expected precision on fg of about 0.1 in 2 independent redshift bins around z = 3, ascompared to errors of about 0.02 for the larger z = 1 survey (see Figure 3-17). Depending on progress inour collective understanding of dark energy, this could be extended subsequently for a more ambitiousz = 3 BAO project.
The high redshift survey target allocation efficiency will be similar to the low redshift survey.
Angular clustering will be less, however, so we dispense with the two-tier observing strategy and instead
rely on a single observation per field at an average completeness of 67%. The result is that 69 fields must
be observed to reach 105 galaxies.

3.7.4 Requested TimeWe now review the observing time required for the proposed surveys. Much of the technicaldiscussion essential to understanding our conclusions is deferred to Section 6. Therein we find an
integration time of 15 minutes is sufficient to locate and recognize [O II] emission in the z~1 survey. Thishas the benefit that overheads for acquisition and readout are negligible in comparison, so that one could
access four fields per hour. For the z~3 survey, the associated integration time is 4 hours, so one canaccess two fields per night.

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Figure 3-17: Histograms of the equivalent width of [O II] 3737 emission line derived from DEEP2 galaxies
in two different redshift bins. This indicates that an 80% redshift success rate will be achieved for the z =1 survey provided a line of equivalent width as small as 20Å can be reliably detected.

Spectroscopic redshift determination for the low redshift survey will be provided by the [OII] 3727Å
doublet, demanding an observed wavelength range of approximately 6330–9700Å. The use of broadband
color information to predict [OII] equivalent widths ahead of spectroscopic observations is discussed in
Section 6. There are no strong constraints on completeness other than what is necessary to maximize
survey efficiency. A resolution of R ~3500 allows identification of the [OII] doublet and sufficientvelocity precision for both BAO and RSD applications. In Section 6 we show that a signal to noise ratio
greater than 7 on the emission line is achievable for blue galaxies with RAB = 23.0 in our planned
exposure times and will reduce the fraction of incorrectly determined redshifts to less than 1%. Similarly,
for the z = 3 LBG objects, S/N = 4 can be obtained for RAB = 25 in 4 hours. Given the strength of spectralfeatures in these objects, simulations discussed in §6 suggest this is adequate. For LRGs with R(AB) <23.6, however, we only obtain S/N = 3. This is insufficient for absorption-line redshifts; the feasibility
study set a target of S/N = 10, which seems realistic, and this would surely not attained in 4 hours
integration. Thus targeting this population seems inefficient.

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These figures yield the following requirements in terms of clear 10-hour nights in order to execute thesurveys we have described, assuming 1400 fibers per field:•z = 1: 832 hours / 83 nights (4,000,000 redshifts)•

z = 3: 286 hours / 29 nights (100,000 redshifts)

Of course, for faint objects, spectroscopic quality is more often limited by fidelity of sky subtraction.
The ‘nod-and-shuffle’ technique was developed at the AAO to improve the normal indifferent
performance of subtraction when using fibers, and it works very well. However, this improvement comesat the cost of a factor 4 in integration time for a given S/N: this factor is not included in the above time
estimates. Given that the required exposure times are already long, it is clear that nod-and-shuffle has tobe considered unacceptably expensive for the surveys under discussion here. In Section 6, we show, via
simulations, that the principal component technique introduced by Wild & Hewett (2005) can dispose ofsky residuals in SDSS spectra, given a large enough library of sky spectra. We thus propose to dispensewith the inefficient ‘nod and shuffle’ technique.
Even given clean spectra of the desired S/N, one problem that will be faced by WFMOS surveys (andall competing spectroscopic dark-energy probes) is achieving high fidelity in assigning correct redshifts.
The problem of emission-line identification in the context of the DEEP2 project is described by Kirby etal. (2008). There are four principal lines visible in galaxy spectra at intermediate redshift: Hα (6563), [OIII] 5007, Hβ 4861, [OII] 3727. For the spectral setup proposed here, these are visible in the redshift
ranges 0–0.5, 0.27–0.94, 0.30–0.99, 0.7–1.6. For z > 1, therefore, we will rely on [OII] 3727 to determine
redshifts; at lower redshifts, some cases will show several lines, but only one may be detected at lower
signal-to-noise. It is clearly essential that the line be correctly identified, since otherwise there is thepotential for systematically transcribing a copy of the density field to an incorrect distance. Such “linelocking” could have potentially serious consequences for the BAO test, since it would alter the estimatedacoustic scale in a systematic way. As an illustrative example, suppose we had a shell of galaxies at
z = 0.8 emitting in Hβ; if the line is misidentified as [OII] 3727 at z = 1.35, this raises the inferred
comoving distance by nearly 50%. Making this error in only 2% of cases then holds the potential(depending on the exact method used) of perturbing the acoustic scale by 1%, which is the signal of
cosmological interest. The correct line identification can be derived via a combination of resolving the
OII doublet (required resolution R > 1340) and using photometric redshifts to reject incorrect candidate
redshifts. In DEEP2, the failure rate in this process is at the dangerous 2% level, and WFMOS surveys
will need to do better. This is not a severe challenge, since DEEP2 worked with BRI photometry, and we
can expect to have superior photometric data (Section 6).

3.8

Summary of Survey Strategy for Dark Energy and Modified Gravity
In the foregoing sections, we have discussed the two main signatures of fundamental cosmologicalimportance that one can expect to detect in large galaxy redshift surveys using WFMOS: Baryon Acoustic
Oscillations as a standard ruler, and Redshift Space Distortions as a measure of the growth rate of density

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fluctuations. In both cases, the optimal kind of survey to perform depends on exactly what kind of
deviation from orthodoxy we think is most likely, but some useful general points have emerged. The casefor a high-redshift survey seems stronger from the point of view of redshift-space distortions, where theindependence of data at z = 1 and z = 3 is substantial. Conversely, the BAO constraints from a z = 3
survey are only substantially different from those of a survey at z = 1 in the case of dark energy whose
properties evolve strongly at high redshift. Until we are able to reject the simple w = –1 cosmologicalconstant, the high expense of a >1,000,000-redshift survey at z = 3 seems difficult to justify.
Based on these considerations, plus the need to deliver a substantial advance on datasets likely to beavailable at the time of WFMOS commissioning (principally the 1,000,000 LRG redshifts at z < 0.8
expected from BOSS), we have evolved a strategy that proposes two surveys of rather different size andcost:
1. 4,000,000 galaxies at 0.7 < z < 1.6 over about 3200 deg2.
2. 100,000 galaxies at 2.5 < z < 3.3 over about 100 deg2.
The first survey should require about 83 nights; the second about 29. The cosmological constraints
from the high-redshift survey will less strong, but will represent a step into novel territory as far as
growth-rate measurements from redshift-space distortions are concerned. The larger z = 1 survey willmeasure growth rates much more accurately, and will also deliver dark energy constraints from the BAO
method that will be at least a factor 2 better than any competition, especially at the upper end of its
redshift range. Both these datasets will have substantial applications in astrophysical aspects of galaxy
clustering. At a cost of 30–40 nights per year over 3 years, these surveys seem realistic as major flagshipprojects for WFMOS and Subaru.

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4

STELLAR ARCHAEOLOGY AND GALAXY GENESIS

4.1
Science Case
4.1.1 Overview
The origin and evolution of galaxies like the Milky Way and M31 remain among the key unansweredquestions in astrophysics. The galaxies we see within the Local Group are valuable representatives of theextragalactic population and have been evolving for the majority of cosmic time. As our nearest neighbors
they can be studied in far more detail than their distant counterparts, and hence provide our best hope forunderstanding star formation and prototypical galaxy evolution (Freeman & Bland-Hawthorn 2002).
Although significant observational progress has been made, we are still a long way from understanding
the details. Detailed large area spatial, kinematic and chemical surveys of nearby stellar systems will
provide the essential link with results from the high redshift Universe. ΛCDM cosmologies predict that
hierarchical merging of dark mater haloes is the main driver in galaxy formation and evolution; indeed thediscovery of the tidally-disrupted Sagittarius dwarf (Ibata et al. 1994) provided the first compellingnearby evidence. However, the detailed process by which large galaxies such as the Milky Way arrive attheir current state is still largely speculative (e.g., Abadi et al. 2003, 2006; Bullock and Johnston 2005;
Font et al. 2006) .
Current large scale spectroscopic surveys such as RAVE (Steinmetz 2003) and the M31 kinematicsurveys on Keck (Ibata et al. 2005; Guhathakurta et al. 2005) highlight the potential of multi-object
spectroscopy to probe the structure and properties of the Galaxy and M31 at modest resolution, while
FLAMES on the VLT provides a compelling argument for the multiplexed use of mid- to high-resolution
spectroscopy, particularly in understanding nearby dwarf galaxies (Tolstoy et al. 2004). However,
although FLAMES is producing exquisite spectra of hundreds of objects, its surveys are still at least anorder of magnitude short of the requirements necessary for a complete analysis of the Galaxy and M31.
Each of the above efforts is necessarily limited in either coverage or depth. While future proposed
Galactic surveys such as the Bulge with AAOmega (PI Freeman: proposed) and the ESA cornerstone
space mission Gaia (Perryman et al. 2001) will go some way to alleviate this problem for the Galaxy, they
still leave unexplored a huge volume of parameter space that holds the key to full understanding of
Galactic evolution.
The diversity of current and planned surveys illustrates the scientific importance and community
interest in this field and WFMOS presents a unique opportunity for a major advance in our understandingof the origin, chemical evolution and mass assembly of nearby galaxies. The key advantages of WFMOS
over existing 8m-class facilities are the greater multiplexing and wavelength coverage together with themuch larger field-of-view. This provides enormous leverage in Galactic studies by enabling a range ofstructural probes in multi-parameter space sensitive to both dynamical and chemical properties.

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Two of the key challenges facing modern astrophysics are our understanding of the fundamental
nature and properties of dark matter, and of the inter-related complex baryonic processes that led to theformation of galaxies like the Milky Way. Although dark matter likely guides the gaseous matter into
proto-galactic environments, baryonic physics governs processes on small scales, through cooling,collapse and star formation. The role of both the dark and baryonic components can be simultaneously
addressed with large-scale WFMOS surveys of the stellar components of the Milky Way and companion
galaxies. In addition to providing tests of cosmological models on low mass scales, such surveys will also
unravel the complex process of hierarchical formation wrapped within various Galactic structures. Thesestudies directly complement the dark energy aspects of this document, and taken as a whole WFMOS will
provide a new window on all three major energy constituents of the Universe.
Galaxies are dynamically and chemically inhomogeneous; only by using the higher dimensionality
afforded by combining kinematic and chemical measures can a full understanding of their constituent
parts be achieved. The fossil record of past events is recorded in the spatial distribution, kinematics andchemical fingerprint of individual stars. As an illustration of the complexity of the Milky Way, Figure 4-1
shows the state of the art in tracing substructure in the Galactic Halo from large area photometric surveys.
Several giant streams (up to ~60 degrees long) from disrupting globular clusters and satellite dwarf
galaxies criss-cross the halo providing valuables probes of both the nature of dark matter and thegravitational potential of the Galaxy.

Figure 4-1: The field of Streams from Belokurov et al. (2006) showing SDSS stars having (g-r) < 0.4 fora region around the North Galactic Cap. Colors denote depth along the line-of-sight with red being themost distant objects at d~50 kpc and blue the nearest at d~10 kpc. The right-hand panel shows acomposite including the 2MASS M-giant distribution from Majewski et al. (2003). Several streams arereadily visible.

4.1.2 Redesigning the Galactic Archaeology (GA Surveys)
To address the nature of dark matter and the baryonic processes we must obtain both kinematic andchemical signatures of large populations of stars. A low resolution survey will yield the kinematics and50

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chemical abundances of stars over a large volume, and an overlapping higher resolution study will
uncover more detailed patterns in their stellar chemistry 1 . The synergy of two such Galactic surveys will
provide the most detailed picture of the processes that formed our Galaxy and of the nature of its
constituents.
Although the Feasibility Study provided an excellent starting point in highlighting the huge potential
gains available for Galactic science, the ambitious nature of that program led to a rather imbalanced set of
proposed surveys. After independently reassessing the science goals and the technical requirements
needed to meet them, we have concluded that the approach adopted in the Feasibility Study is not
optimal.
The science drivers for Galactic Archaeology (GA) with WFMOS still naturally split into two
components:•

Low resolution (LR) kinematic and general abundance analyses of Galactic dynamical structure;•

Detailed high resolution (HR) analyses of constituent populations to trace temporal chemicaldevelopment;

but the detailed strategy within each is somewhat different.

4.1.2.1 LR Survey
The LR mode will focus upon obtaining accurate kinematics and metallicity measures for a large
stellar sample. In this mode, the main Galactic survey science for WFMOS (15 <V<20) is complementary
to the Gaia (V < 15–17) and RAVE (V <12) large area surveys. Gaia synergy is especially importantsince for both full kinematic and HR abundance analyses, good distances and accurate proper motions are
required. Matching the LR survey depth to that of Gaia, i.e., to V~20, is thus a sensible approach.
Good velocity accuracy is essential for identifying streams and substructure against the Galactic
background population. As an example, the required velocity errors for 3-sigma detection of streams foran n-dimensional kinematic survey are:
ε=

σ
3

×

1
N 1/ nwhere σ is the dispersion of the background population, and N is the number of streams (e.g., Helmi &White 1999). For a survey limited to radial velocity information this implies that errors of ≈0.1 km/s are
required to locate halo streams in the Solar neighborhood (assuming σ = 100km/s and N = 500; Wilkinson
et al. 2005). Adding proper motions improves this dramatically, implying required errors of about ≈4
km/s for Halo substructure. For disk streams the radial velocity error would need to be ≈2 km/s.
It should be noted that while Gaia will revolutionise this field, it will not provide detailed abundances andaccurate radial velocities to fully explore Galactic history. WFMOS will thus fully complement the Gaia andsignificantly enhance its scientific impact.
1

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Satellite galaxies have velocity dispersions in the range 6–12 km/s whilst star clusters range from 1
km/s (open clusters) to 10–20 km/s (massive globulars). As the velocity dispersion of tidal tails of
disrupting systems reflects that of the progenitor, detecting and analyzing dissolving clusters therefore
also requires errors of a few km/s. Additional complications arise from the smooth foreground population(e.g., Brown, Velazquez & Aguilar 2005) against which it is harder to pick out substructure. Abundance
measures will help differentiate substructure and again velocity errors of ~2 km/s are necessary.
An efficient mid-resolution (R=5000) spectroscopic survey, comparable to FLAMES in LR mode,
will deliver velocities to the required ~2 km/s precision and also provide proxy [Fe/H] measurements to
0.1 dex via the Mgb and Calcium triplet (CaT) diagnostics. Although data of this resolution will notprovide accurate evidence of more exotic alpha-, r- and s-process element measures for individual stars, it
alleviates the need for an unfeasibly large HR sample. It would also enable the tracing of the dissolution
and tidal debris from clusters (where km/s precision is needed) and detailed studies of halo substructure
and the nature of dark matter.
Similar reasoning holds for surveys of the M31 and M33 galaxies. The current ongoing surveys
sensibly make use of modest resolution spectrographs to derive both kinematic and population-averaged
overall metallicity measures. It is now well known (e.g., Tolstoy et al. 2004) that individual velocity and[Fe/H] parameters are required to unravel the complex star formation histories in dwarf galaxies. The
same will inevitably be true for their giant neighbors. A further possibility, as evidenced by Kirby et al.
(2008), is that by combining spectra from similar kinematic components, modest R=5000 resolution
spectra, but at high signal:to:noise, could be used to extract additional chemical information, such asalpha-element ratios, by being more creative with analysis tools such as ICAs or specifically trained
ANNs (e.g., using currently available FLAMES spectra) to extract this information.
In contrast to the above, the LR survey proposed in the Feasibility Study case offered a velocity
precision of ~10 km/s which we believe is too coarse to detect tidal streams and accurately assign
population membership. At the low resolution proposed, accurate equivalent width (EW) estimation is
also impractical. In our case, a resolution of R=5000 in the Mgb and CaT regions opens up the parameter
space significantly given spectra of reasonable signal/noise. We will achieve a velocity precision of a few
km/s, [Fe/H] estimation to 0.1 dex and constraints on alpha-element ratios. This is a much better match tothe impressive Gaia datasets about to be delivered.

4.1.2.2 HR Survey
While the original Feasibility Study proposed a HR mode with R = 40,000-50,000 with very limited
wavelength coverage, we are proposing a much larger simultaneous wavelength coverage but at aresolution of R =20,000. We have arrived at this conclusion by examining the extraction of chemical
abundances from degraded high resolution spectra (Irwin and Lewis 2009; in prep.), and studies withVLT FLAMES, Keck HIRES and MIKE Magellan. These collectively demonstrate that `chemical

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tagging’ at R = 20,000 is feasible and by focusing on redder wavelengths (4800--6800 Å) than thosehighlighted in the Feasibility Study, studying a million stars is a practical proposition.
Our proposed HR surveys will yield data similar to the VLT FLAMES/GIRAFFE instrument
operating in its in HR mode (where we already have built up a lot of experience), but with theconsiderable advantage of covering the full wavelength region of interest (4800–6800 Å) in a single
exposure. By working in the “visible” regime, line blending is lessened. A good example is provided by
the forbidden oxygen line [OI]6300.3, shown in Figure 4-2 from a 5400s VLT-FLAMESR=20,000 spectrumof a metal-rich Bulge giant. Although the [OI]line is half-blended with ScII6300.7, it is still sufficiently
distinct to quantify using multi-component profile fitting.
The advantage of contiguous wavelength coverage over 4800–6800 Å is access to a dozen FeII lines
and more than a hundred FeI lines from which accurate stellar atmosphere parameters can be determined:
effective temperature; surface gravity; metallicity and microturbulent velocity; using excitation andionization equilibrium and known curves-of-growth. The most important elements for chemical tagging
all have measurable lines in this region. The availability of parallaxes from Gaia and broadband opticaland near-infrared photometry will also play a significant role in this detailed stellar atmospheremodelling.
Combined with the 5- to 10-fold increase in target allocation this produces a 25- to 50-fold increase insurvey efficiency over the VLT, enabling a range of GA projects that would be impractical with current
instruments.

Figure 4-2: The region around the [OI]6300 line from a 5400s VLT-FLAMES exposure at R=20,000 of ametal-rich, [Fe/H = -0.41 Bulge giant with S:N ~200/A. An unconstrained multi-component Gaussianmodel fit of the most prominent lines is shown overlaid in red.

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As an illustration of why R = 20,000 is sufficient for our purposes, we compare the model fit secured
using the data shown in Figure 4-2, with that obtained at a similar signal/noise at R = 50,000 using the redarm of VLT-UVES and the same overall exposure time. A comparison of the unconstrained model fit
parameters with the visually measured and identified lines, shown in Table 4-1, demonstrates that
automatic model fitting at R =20,000 can give comparable results to visual interpretation of a muchhigher resolution spectrum and that EWs to better than 5mÅ precision can be obtained. The main
limitation for such metal-rich stars is not partially unresolved lines but the continuum placement. Here theerror in continuum placement is about ~0.01 in normalized units leading to a typical EW error due to thisalone of about 3 mÅ. Further improvements in the model fitting are also possible by constraining therelative wavelengths, particularly of blended lines.
A further significant advantage of a resolution of R = 20,000 is that by adopting a low order crossdispersed spectrograph design it is feasible to do quantitative spectrophotometry over large extended
spectral regions. This is difficult in conventional echelle-mode instruments and can be used, for example,
to tie in measurements of spectral indices (often all that is available for distant galaxies) with detailed line
analysis. As an example, the Lick indices of Mg2, Fe5270, Fe5335 are widely used in extragalactic work
(e.g., Worthy et al. 1994) and require fluxed spectra covering a wide spectral range. To measure the Mg2
index at 5156–5197 Å requires continua defined at 4897–4958 Å and 5300–5366 Å therefore requiringcontiguous coverage from 4897 Å to 5366 Å. A large spectral coverage also makes it feasible to analyze
the extended wings of the Balmer lines to derive more accurate effective temperatures and surface
gravities.
Table 4-1: FLAMES-GIRAFFE R=20000 EWs compared to UVES R=50000
λFLAMESUVES

EW(m Å)

FWHM (Å)

EW(m Å)FeI

6297.84

122.19

0.36

123.2

OI

6300.32

39.99

0.30

41.4

ScII

6300.65

26.80

0.38

25.4

FeI

6301.53

128.92

0.33

126.9

FeI

6302.49

117.73

0.36

101.0

TiI

6303.81

72.33

0.43

79.5

We also note that absolute flux calibration is not necessary since it is straightforward to use extant
photometry to provide the conversion from relative to absolute fluxes and thence spectrophotometry.

4.1.2.3 Summary of GA Survey Requirements
The above considerations lead to a well-defined set of technical requirements for the GA science
goals. Noting the kinematic synergies with Gaia, these are summarized in Table 4-2.

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Table 4-2: Summary of the Galactic Archaeology Requirements
LR survey to V = 20 (distances, proper motion, radial velocity, [Fe/H])
HR survey to V = 17 (uniqueness of abundance analysis for chemical tagging
LR(Extent of halo, ancient streams, disk structure, Fe/H)
Velocities to ~ 2-3 km/s, [Fe/H] to ~0.1 dex, ~2400 fibres
R = 5000, CaII triplet 700Å from 8150-8850Å & Mgb region 4800-5500Å
Npix = 2x2000, V < 20, 0.5hr exposures 2000 deg2, ~3 106 stars, 765 hours

HR(Chemical tagging of elements, IMF, SF history)R = 20000, covering 4800-6800Å, Npix = 20000
V < 17, 1hr exposures 2000 deg2, ~106 stars, 1125 hours
FWHM 3 pixels, pitch ~8 pixels, ~600 fibres

4.1.3 Addressing the Big Questions
We now develop a fuller account of the science case for our two surveys.
Low Resolution (LR) Survey: This will result in a detailed kinematic and general abundance map of

the Galaxy for which WFMOS depth and areal coverage are crucial. The major scientific goals for thissurvey are:
What is the nature of dark matter?

While our understanding of dark matter appears to be adequate on cosmological scales, its application
is being challenged on the scale of individual galaxies. The WFMOS survey will focus on several crucial
galactic-scale properties of dark matter. These are:•

The mass and extent of the MW dark matter halo:
The shape of the dark matter halo of the Galaxy is a consequence both of its formation history
and the very nature of dark matter itself, and the Milky Way is the one massive spiral galaxy inwhich we can hope to map out the distribution of dark matter in fine detail. However, themajority of kinematic tracers currently available are tied to the Galactic disk, and the shape of theoverall potential is very poorly constrained. Recent advances using tidal streams have met withsome success (e.g., Fellhauer et al. 2006), although the paucity of data has ensured that arguments
over the shape of the dark matter distribution in the Milky Way persist. Little progress will bepossible until after Gaia as beyond 50 kpc there are currently only a few tens of tracers (dwarf
galaxies, globular clusters, halo stars; e.g., Wilkinson & Evans 1999; Battaglia et al. 2005). Gaia
will provide a few thousand objects out to 60kpc, but WFMOS will push the radial velocities
much further out (to V = 20 where there will still be proper motion constraints from Gaia);
WFMOS will also measure velocities for high luminosity carbon stars, AGB stars and TRGB

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stars to distances to 250 kpc, with several tens of objects expected in a 1000 deg2 survey - Gaia
will provide proper motions for these stars out to 60–80kpc. These new tracers will make itpossible to place significantly stronger constraints on the mass and extent of the Milky Way halo,
as well as enabling a detailed study of the velocity distribution of halo stars. Furthermore,WFMOS observations of the disk of the Milky Way will provide a detailed characterization of themass distribution close to the Galactic Plane, and it will be possible to investigate whether the
Galaxy has a central cusp or core in its dark matter distribution. Since cusps are a genericprediction of galaxy formation in ΛCDM cosmologies, the existence (or lack thereof) of dark
matter cores is a fundamental probe of our underlying cosmological ideas. Current data on the
Galactic rotation curve and microlensing optical depth suggest that cusped models cannot
reproduce the observations (Binney & Evans 2001). However, the detailed stellar kinematics
provided by WFMOS will provide an essential probe of the dark halo profile in the central
regions as they are implicitly dependent upon the form of the dark matter profile.
•

The lumpiness of dark matter from tidal stream properties:
As globular clusters dissolve in the tidal fields of massive galaxies, the stripped material forms
into extended, cold stellar streams. Such cold streams are very sensitive to kinematic heating anddispersal through interaction with ~kpc substructure within the dark matter halo, while the hotter
streams of dwarf galaxies are significantly more robust. Recently, extended tidal tails have beenfound associated with globular clusters, including Pal5 and NGC5466, and more examples arecoming to light in extensive imaging surveys (e.g., Belukorov et al. 2006; Grillmair & Dionatos
2006). As cold young tails may morphologically resemble older, disrupting streams, it is vital todetermine the heating of streams along their length, as this reveals their true nature and theinteraction rate with any dark matter substructure. The WFMOS LR survey provides an ideal
opportunity to kinematically map satellite tidal streams and hence constrain the dark matter
substructure of the Milky Way halo, and hence the nature and number of dark satellites (Gilmore
et al. 2007).

•

The missing satellite problem - dark satellites:
The dwarf spheroidal galaxies (dSphs) of the Local Group are valuable laboratories in which totest the properties of the dark matter, as they are extremely dark matter dominated in their inner
regions (e.g., Kleyna et al. 2001, Wilkinson et al. 2004, Mateo et al. 1998) and appear to be thesmallest stellar systems that contain dark matter in dynamically significant quantities. The
number of low mass dark matter haloes in the vicinity of a Milky Way-type Galaxy is a strong
function of the nature of the dark matter - cold dark matter (CDM) simulations typically overpredict the numbers of dSphs around the Milky Way by at least an order of magnitude (e.g.,
Moore et al. 1999) while warm dark matter models contain significantly fewer low-mass haloes(e.g., Bode, Ostriker & Turok 2001); this is known as the “CDM satellite crisis.” The recent

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discovery of the extremely low luminosity Ursa Major dSph (Willman et al. 200, Kleyna et al.
2005), Boötes (Belokurov et al. 2006) and Canes Venatici (Zucker et al. 2006) satellites raises theinteresting possibility that a number of dSphs containing very small stellar populations might
have evaded discovery in previous surveys. However, only kinematic measurements can establish
definitively whether an object is a dSph or a star cluster. WFMOS will be the ideal instrument forspectroscopic follow-up of new dSph candidates as wide-area surveys of stars to faint magnitudes(V = 20–21) are required to map the entire system and to provide spatially resolved velocity
dispersions and abundance distributions. A candidate dSph with a low-luminosity (MV ~–5) and alarge half-light radius (~200pc) would have a velocity dispersion of <1km/s if it were a selfgravitating system containing no dark matter, while all dSphs observed to date have velocity
dispersions in excess of 6 km/s. The existence of a previously unknown population of dSphs
would be a major victory for CDM hierarchical structure models. For the nearer satellites, Gaia
will provide proper motion measurements of the brighter member stars. This will enable direct
measurement of the velocity ellipsoids throughout the system and thereby break the mass
distribution-velocity dispersion anisotropy degeneracy.
•

The nature of dark matter “particles” from satellite profiles:
The masses of dark haloes of dSphs can be used to constrain the properties of the dark matter,with the mass spectra of subhalos dependent upon its temperature. Furthermore, the density
profile of the subhalos is also temperature dependent, with warm dark matter haloes typically of
lower concentration than those found in CDM simulations (Bode et al. 2001). Hence it is essential
to obtain a robust estimate of the total masses and densities of the known dSphs. Significant
observational effort over the past decade has produced large kinematic data sets in dSphs which
are being used to constrain the mass distribution within the main body of each system - the shapeof the density profile can be used to constrain the physical properties of the dark matter particles.
However, to complete the picture, we must also determine whether the dark matter haloes ofdSphs extend beyond the edge of their main stellar distributions, in a similar manner to the haloof the Milky Way and other massive galaxies. The outer parts of most, if not all, dSphs exhibit
signs of tidal disturbance in their stellar distributions (e.g., Carina: Majewski et al. 2005).
However, the stellar kinematics in the outer regions remain controversial (see Wilkinson et al.
2004, Münoz et al. 2005 for the case of Ursa Minor). Wide-area surveys of the outer regions ofall dSphs are beginning (e.g., Walker et al. 2008), but achieving deep, wide-field spectroscopy isa task well-suited to WFMOS, allowing us to trace dwarf stellar kinematics well beyond theirnominal tidal radii. If the apparent disturbance of their outer stellar distributions is indeed due tothe tidal effects of the Milky Way (which could be confirmed, for example, by the detection ofphotometric and kinematic tidal tails) then their total masses cannot be much larger than a few
108M. On the other hand, if they are found not to exhibit evidence of tidal perturbation this57

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would argue strongly that their masses are in excess of 109M, in which case the “CDM satellite
crisis” may well be resolved.
The mass and extent of the MW stellar populations

One of the primary goals of the WFMOS LR survey will be to dissect the Galaxy into its fundamental
components, delineating structures based upon kinematics and metallicities. Without this deconstruction,
understanding the detailed formation of the Milky Way is intractable. Such a survey will also reveal thepresence of substructure, especially in the form of old, in-plane accretions. Furthermore, we only know
the velocity distribution in the disk in the Solar neighborhood, although a global understanding of disk
kinematics is required to understand its origins. A combination of Gaia and WFMOS will extend
kinematic knowledge out to the edge of the disk in the Galactic anti-centre direction; Gaia alone cannot
do this as the radial velocities do not probe far enough out. The resulting dataset will, for the first time,
provide answers to the major questions about the Milky Way, including; what features in the velocity
distribution are resonances due to the bar (Dehnen and others have claimed some of the local features are
bar-related - if they are resonances we should not see them at other radii)? How much substructure is in
the disk? Can we see the effects of transient spiral arm passages through the disk? What is the variation of
the velocity ellipsoid as a function of position in the disk? These will provide the important clues to theformation of the Galactic components.
The current generation of bulge surveys, such as those using the AAOmega spectrograph (PIFreeman) and the Bulge Radial Velocity Assay (BRAVA) with HYDRA on the CTIO 4 m (Howard et al.
2008), will provide first order maps of the kinematics of the bulge (velocity dispersion and mean velocity
as a function of position through the bulge), as well as detailed metallicity distribution function. However,
the much larger samples provided by WFMOS will make it possible to investigate in detail the secondorder kinematic structure seen in simulations of the Galactic bulge (e.g., Figs. 10 and 16 of Fux 1997).
These velocity features depend on the details of the stellar structure through the bulge. The large samples
will also make it straightforward to detect the signatures of the resonances that may have produced thepeanut-shaped bulge of the Milky Way (Quillen 2002) as well as other resonant features.
Is the Milky Way Representative?

Is the Milky Way representative in its history and overall properties? To answer this question, any
survey of the kinematic and chemical abundances of the Galaxy needs to be compared to similar studies
of other spirals. In practice, this is impossible for all but the nearest spirals, M31 and M33. WFMOS
offers the only realistic prospect of a panoramic spectroscopic survey of the stellar populations of ournearest large galaxies.
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Large area M31/M33 velocity and abundance survey—e.g., outer halo M31:

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While M33 is distinctly different to the Milky Way, being an order of magnitude less massive andapparently undergoing a more quiescent assembly, the similarity and differences between M31
and the Galaxy have long been debated. With 8–10 m class telescopes, the ability to target thekinematics and chemistry of individual populations of stars, has led to a revolution in Galacticarchaeology. Both galaxies appear to possess similar overall structures, in the form of metal poorstellar halos and complex extended stellar disks. By the time WFMOS is built, it is envisaged that
dedicated surveys by DEIMOS/GMOS-class instruments will have provided detailed kinematicand chemical maps of the bulge and disk structures in these galaxies, revealing details of theunderlying stellar halo that will contaminate any study. Furthermore, current studies are alreadytargeting significant substructures that are readily apparent in photometric maps of the outer diskof M31. As with the Milky Way, the halos of M31 and M33 hold great promise for unraveling theaccretion history through the identification of extremely low contrast tidal streams in kinematicand chemical space. Again, such studies will also probe the underlying dark matter potential ofthese galaxies, providing further clues as to whether the Local Group dark matter distribution
matches expectations from ΛCDM cosmological models.
However, given their small field of view, it is impractical for DEIMOS/GMOS-class instruments
to efficiently survey the sparse halo regions and this is clearly a region where the capabilities of
WFMOS will provide a unique probe. Deep photometry (currently ~80 deg2 from the INT/WFC
and CFHT/MagaCam; Ibata et al. 2007; Figure 4-3) of the extended halos of both M31 and M33,
is steadily accumulating, and down the SE minor axis of M31 already extends along the entire
line-of-sight to the centre of M33, a distance of ~220 kpc (15 degrees on the sky). These surveys
are revealing an astonishing wealth of substructure, even in the outer halo. When complete
coverage, ~500 deg2, is obtained in the next few years this unprecedented dataset will enabledirect testing, via targeted efficient spectroscopic kinematic and metallicity follow-up surveys, ofthe predictions of ΛCDM cosmologies. Are fainter more diffuse tidal streams and relict debris
dominant throughout the halo? What is the shape and total mass of the dark matter halos of M31
and M33? Will this explain the puzzling apparently quiescent history of M33? WFMOS will be
the only instrument that can tackle these important questions within realistic time allocations.

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Figure 4-3: The INT/CFHT survey of the southern halo regions of M31; M33 can be seen in the lower
left-hand corner of the image. The stellar haloes of M31 & M33 overlap and are replete with
substructure (Ibata et al 2007).

High Resolution (HR) Surveys: A major strength of WFMOS is the ability to exploit the naturalsynergy between targeting deeper surveys of stellar structure at low resolution with the more detailed
picture afforded by the shallower higher resolution spectroscopy. For this type of work WFMOS/HR will
provide a unique facility for undertaking the necessary detailed chemical tagging of large samples ofstars, enabling deconstruction of the entire formation history of the Galaxy.
The primary question here is:
How has the Milky Way system evolved?

The formation and evolution of large scale structure channels the building material of galaxies, bothdark matter and baryons, into high density environments. While this provides the birth site for galaxies,
our understanding of ‘What happens next?’ is less clear. The combined LR and HR surveys withWFMOS will enable us to test the various scenarios that brought the Milky Way into existence. The fossil
record of the chemical evolution of entire stellar populations and their subsequent dispersal around thegalaxy is within reach and is opening up a new era of Galactic astronomy.
•

The evolutionary history of stellar components and substructure:
The HR surveys are crucial since they are required to fully unravel the formation history of
Galactic populations. Chemical tagging through the accurate determination of chemical
abundance ratios allows the identification of common formation sites for disparate, dispersed

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populations and allows a detailed analysis of the physical processes governing star formation andevolution. Whereas the metallicity distribution of stars samples the chemical enrichment historyof the galaxy, age estimates for individual stars provides information on the evolution with timeof the abundances for a range of chemical elements. This impacts a wide area of astrophysics
since the detailed progression of different species is directly affected by initial mass functions,star formation rates, infall and outflow of gas, nucleosynthesis pathways and stellar lifetimes.
•

The role of star clusters in structure formation:
All stars form in clusters or associations. If we can reconstruct these using chemical tagging, thatwill be a valuable piece of information for star formation models used in cosmologicalsimulations and will have relevance for feedback, galactic winds, etc. By reconstructing recently
disrupted clusters we can determine current and recent cluster mass functions by combining
kinematics and abundances. In addition, distances from Gaia combined with photometric
information from wide field optical and near-infrared surveys hold out the tantalizing prospect ofage dating stars in groups and thereby linking coeval star clusters over the entire history of the
Galaxy.
Kroupa & Boily (2002) have emphasized the role that gas expulsion has on young cluster
disruption and hence on the mass function of clusters that survive beyond star formation. This canbe tested by looking for haloes of loosely bound or unbound stars near open clusters.

•

Constraining nucleosynthetic pathways, sites, and stellar yields:
For most elements uncertainties remain about their formation sites, the nucleosynthetic pathways
from which they are produced, and the yields from a given site. The detailed abundance ratios ofstellar populations of different environments impose very strong constraints on the different
processes that produce elements in stars (e.g., the IMF and SFH), improving our analytical poweron the chemical evolution of the galaxies.

•

Signatures of ancient accretions:
The importance of combining kinematic and abundance information for tracing the members of
disrupted systems has long been recognized. Stars now distributed far from their birthplace can be
identified by tracking their location in a combined chemo-dynamic phase space. An excellent
example of this is provided by the detective work of Helmi et al. (2006) in unraveling the chemodynamical signature of the dispersed members of several groups in the Galactic disk. Withdetailed chemical abundance ratios, many of the ambiguities and degeneracies present inkinematic signatures can be resolved, enabling detailed allocation of various stellar components
thereby allowing traceback to a common formation sites. Stars from common progenitors show
distinct correlations between their orbital parameters, their abundance ratios and evolution withage, and can be directly used to probe evidence of the hierarchical formation of the Milky Way.

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The properties of extreme metal poor Pop III stars:
During the gaseous collapse of the Milky Way, the first generation of stars was left in a roughly
spherical halo surrounding the newly formed disk, and this population of extremely metal poorstars are being uncovered today; although current measurements are limited mainly to stars
passing through the Solar neighborhood, Christlieb et al. (2003) report the identification of anumber of ultra-low metallicity stars associated with the Galactic halo. These stars provide a
direct link to the conditions in the protocloud from which our Galaxy was formed. The sparsenessof the halo population, however, ensures the progress in the discovery of this population is very
slow. Intriguingly, simulations of Galaxy formation suggest that the earliest pre-galactic stars will
be concentrated in the central regions of the Galaxy at the present time. The photometricmetallicity distribution function (MDF) in the Zoccali et al. (2003) Bulge field shows a peak at[Fe/H] ~0 but with a long tail extending to [Fe/H] ~ -2, and a survey of the Galactic Bulge forvery metal-poor stars should therefore yield a larger sample of such objects than the studies of thehalo. Ongoing surveys, including the Sloan Extension for Galactic Understanding andExploration (SEGUE/SDSSII; Lee et al. 2008) and the AAOmega/AAT Bulge survey (PIFreeman) will begin this important work.
However, the vital key to the earliest epochs lies with the distribution, kinematics and chemical
properties of the rarest, ultra-low metallicity stars. Here only a combined LR/HR large scale
survey by WFMOS can obtain the spectra of sufficient numbers of stars to obtain a statisticallyuseful sample. The LR survey necessarily acts as a first cut at locating extreme metal poor
whereas the HR counterpart is required to guarantee the nature of the stars and to provide thedetailed abundance ratios needed for atmospheric analysis. Extremely metal-poor stars are fossilsof the first stellar formation and metal enrichment processes. As such there is immense interest in
answering questions relating to the initial mass function of the first stars and the subsequentmechanisms for early chemical enrichment in the Universe.

•

Linking the Bulge to the high Z universe:
The Galactic bulge provides many clues to the nature of our galaxy. Laid down in the initial
phases of the birth of the Milky Way, the bulge is related to spheroidal components of othergalaxies and so establishing how it formed (collapse, accretion, disk instability) has a muchbroader significance in theories of galaxy formation. In addition, the interplay between thevarious processes involved will advance our knowledge of the stellar dynamics of galaxies moregenerally. The integrated spectra of Galactic Bulge fields and of Galactic globular clusters are
directly comparable to those of distant ellipticals and the bulges of spirals as shown in Bica
(1988). Spectral indices indicating abundances of alpha-elements such as Ca and Mg can be
accurately calibrated from the HR spectra and allow a direct comparison between the Galactic
Bulge and distant galaxies (e.g., Worthy et al. 1994). Another link with the high-z universe comes62

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from the measurement of Zn abundances. These are measurable in QSO absorption lines andprobably reflect bulge formation at high-z that can be directly compared to the spatial andtemporal distribution of Zn in Galactic Bulge giants covering a range of metallicities and ages.

4.2

Detailed Survey Design
While a Galactic archaeology survey with WFMOS encompasses a broad spectrum of goals, it isessential that the survey is undertaken to provide maximum scientific return within a realistic time
allocation. Given the complexity of the various goals, no single, efficient survey strategy suffices. In thefollowing sections we investigate the survey requirements needed to meet the science goals set out
previously, based on current knowledge of the Galactic populations and some numerical simulations. The
overall survey will be broken down into distinct components; The Galactic Bulge, The Outer Disk and theGalactic Halo. These will be supplemented with a targeted survey of known streams and othersubstructures, nearby dwarfs and a panoramic survey of our nearest large companions, M31 and M33.
The general strategy will be to undertake a LR survey, followed by a HR survey to obtain detailed
abundances on specific targets e.g., LR to identify metal poor bulge stars, then a HR follow-up andanalysis of pop-III candidates.

4.2.1 The Galactic Bulge
The Galactic bulge provides many clues to the nature of our galaxy. Laid down in the initial phases ofthe birth of the Milky Way, it is expected that the bulge is home to a large population of the first stars thatformed from the proto-galactic cloud. Furthermore, the present spatial and kinematic structure of thebulge, specifically the presence of the bar, has a long-range influence on the stellar structure of the MilkyWay. Unfortunately, most of the stars, gas and dust in the Milky Way are confined to the bulge and plane,
where extinction and crowding making it difficult to unveil the inner structure. Previous surveys haveconcentrated in clear “windows,” where optical surveys can be carried out (MACHO, OGLE, etc),
although success has been varied. Current surveys, including AAOmega Bulge survey (PI Freeman) andBRAVA on CTIO (Howard et al. 2008), are beginning to obtain a more global picture of the kinematicand chemical properties of the Bulge.
Large scale photometric surveys in the near-IR will soon be able to penetrate gas and dust towards the
Galactic centre. Covering a total of 360 deg2, a VISTA survey will identify a ~5 × 108 point sources, withroughly a million variable stars providing distance estimators. While such a photometric dataset will
provide a wealth of information on the bulge, including the 3-D structure, the identification of the earliest
stars and understanding the bulge’s dynamical influence will require detailed spectroscopic follow-up.
It is currently unclear whether the inner Galaxy contains a single bulge component formed via thebuckling and thickening of a bar (Kormendy & Kennicutt 2004) or whether there is an additional, distinct
spheroidal component which built up through accretion (Freeman & Hawthorn 2002). WFMOS canprovide a definitive answer to this question by establishing the dynamical state of the bulge using a large

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sample of bulge stellar velocities and metallicities. By comparing the observed kinematics with thepredictions of dynamical bulge models, (including both descriptive Schwarzschild orbit synthesis models,and fully evolutionary N-body models which track the formation of the bar/bulge via disk instabilities), it
will be possible to understand the detailed properties of the stellar bar/bulge and learn which processes
were dominant in the evolution of this region. Currently available kinematical data are totally inadequate
for this purpose, and a WFMOS survey will provide the first sample large enough to probe the fullkinematic structure of the inner Galaxy.
In order to estimate the number of stars necessary to distinguish different bulge models, thekinematical predictions of an N-body stellar bar (Athanassoula 2005) and an axisymmetric bulge model(Dwek et al. 1995) can be compared. Allowing for foreground contamination, ~1000 bulge stars areneeded to resolve the systematic ~10 km/s differences in dispersion between the models over distances ofabout 1 kpc along a single sightline. The limited size of previous surveys has prevented discussion ofkinematic variations along the line-of-sight. As an example, Figure 4-4 show how the models differ in thevariation of projected velocity dispersion at low Galactic longitude. Because the kinematics vary on
scales of a few degrees, we require the good areal coverage of the region |l|<15o provided by our proposedsightlines. Further, although the location of the ends of the photometric bar are known from star counts,
models of the resonant thickening of bars into peanut bulges predict the presence of resonances locatedwithin the bar (e.g., Quillen 2002); our goal is to map out these resonances through their velocity
signatures.
Clump giants will be used for much of the main bulge survey, because they are numerous in moststellar populations older than about 2 Gyr and have a well-defined relation between absolute magnitude,
age and [Fe/H], allowing accurate estimates of their distances. In Baade’s Window, about half of the stars
with I<16 are clump giants (Paczynski et al 1999). It is easy to measure their radial velocities andabundances, and verify their gravities using the Ca II 850 nm triplet. Clump stars are ideal tracers because
we can measure their distances using reddenings individually determined from the 8620 Å diffuse
interstellar band. The OGLE photometry shows that there are several thousand clump giants per WFMOS
field, even in the outer bulge. Also included will be brighter giants, to measure the kinematics of otherbulge populations, such as younger AGB stars and very old metal-poor stars in the bulge region. The
younger stars are important tracers of any more recent evolution of the bulge. The metal-poor stars in thebulge are particularly interesting, because recent simulations suggest that the earliest pre-galactic stars
will be concentrated now in the central regions of the Galaxy.
While LR kinematic studies will provide important clues in improving our understanding for theformation and evolution of the Galactic Bulge, for detailed comparison to evolution models will require
the determination of the chemical properties of the Bulge population. WFMOS will be employed toundertake a HR survey to (i) establish the shape of the detailed metallicity distribution function (MDF) ineach field, for comparison with chemical evolution models, and (ii) quantify the contribution of the more

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metal-poor stars to the bulge and determine (in combination with kinematics) whether they are part of thebulge or part of the galactic stellar halo. Existing photometric MDFs do not have sufficient statistics forthis purpose: we will dramatically improve this situation with our spectroscopic MDF. The bulge shows astrong vertical abundance gradient, and its overall MDF extends from [Fe/H] = +0.3 to -1.5 (e.g., Zoccali
et al 2003; 2006). WFMOS will provide not only the mean metallicity but also the distribution ofelemental abundances (most notably Fe-peak and alpha-elements) at different locations across the bulge,which will help us understand its chemical evolution. Observing an unbiased sample of giants in six fields
distributed over the bulge, WFMOS will reveal how the MDF changes relative to the mean abundance
gradient. The approach will employ the LR survey to identify candidates of interest (i.e., popIII membersof the bulge) to be followed up in the HR mode, providing abundance fingerprinting of the differing
contributors to the Bulge population.

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Figure 4-4: Dynamical predictions based upon a N-body simulation of a stellar bar and an
axisymmetric bulge model. The comparison assists in determining the observing strategy necessary to
distinguish different models for the Galactic bulge.

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4.2.2 The Outer Disk
The outer disk is targeted for two primary reasons; firstly to characterize its structural properties,
especially the functional decrease in the stellar density and truncation radius. Secondly, the survey will be
used to identify any low latitude tidal streams whose demise will contribute to the thick disk population.
In characterizing the outer stellar disk, the best region to target is toward the Galactic anti-centre, where
we propose to characterize the stellar populations using three survey stripes at l=160o, 180o and 200o,
extending between b=±30o.
Figure 4-5 shows a numerical simulation of the demise of the putative Canis Major dwarf galaxy,produced by Penarrubia et al. (2005). The density excess characterizing the centre of the Canis Major
region and its extension into the Monoceros ring has lead to much debate in the literature as to its nature:a close to in-plane accretion of a dwarf galaxy; or a flared warped outer disk compounded by a possible
outer spiral arm. If a low latitude accretion this would have resulted in extensive tidal debris, reaching±40o. Clearly these would intercept the vertical stripes used to characterize the structure in the outer disk,
and these would allow for the identification of significant substructure, including tidal streams, in theregion of the outer disk. However, the strongest constraint on the evolution of the tidal disruption ofequatorial systems are longitudinal velocity gradients, and hence the two additional survey stripes at ±30o,
extqending between l = 120o and 240o, will be employed to measure these over large portions of tidal
debris streams.

Figure 4-5: The properties of a numerical simulation of the demise of the putative Canis Major dwarf
galaxy and the resultant Monoceros Stream (Penarrubia et al. 2005)

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While kinematics will provide a first step to disentangling the various substructures expected in theouter disk, clear discrimination is only possible with abundance studies; each of the accreted progenitors
will carry its own distinct chemical fingerprint which metallicity through proxy can only provide a firstorder discriminator. Hence, targeted regions of the streams and central body will be observed in HR mode
to provide this information.

4.2.3 The Galactic Halo
The identification of halo streams in important for several of the key science goals of this survey,
namely determining the mass and extent of the dark matter halo, as well as the identification of any
putative dark substructures. Due to their low projected stellar density (>30 mag/sq arcsec), it is extremely
difficult to identify tidal streams in the Galactic halo within photometric surveys; only kinematic andchemical signatures can be effective to locate accreted systems from the Galactic populations.
Figure 4-6 shows the distribution in RA and Dec of debris from a typical inner halo satellite at
12 Gyr. Orbital plane precession due to the non-spherical components of the Galactic gravitational
potential means that the debris is distributed over much of the sky, although the surface density ofmaterial is low and highly non-uniform. Due to their proximity, an individual stream covers a hugeswathe of sky and hence optimizing a survey to maximize the detection and characterization of a
population of streams is not a straightforward task. In the following, N-body simulations (provided by A.
Helmi) are used to construct mock skies such that differing observing strategies can be compared.
The simulations follow the evolution of satellite galaxies orbiting in a static Milky Way potential for
12 Gyrs. A total of 32 satellites with orbits in the inner halo (orbital apocenters less than ~20 kpc) were
followed along with two satellites in the outer halo. All satellites were initially single-component
Plummer spheres containing 100,000 particles. The orbital planes of the satellites were randomly oriented
with respect to the plane of the Galaxy and the initial orbits were strongly radially anisotropic. A detailed
description of the simulations, including the initial conditions, Milky Way model and integration scheme
used is given in Helmi & de Zeeuw (2000). We construct an ensemble of debris trails by generating fiverealizations of each satellite’s debris at 12 Gyr, obtained by first rotating in azimuthal angle and thenobserving the debris from the position of the Sun.
In order to establish the number of stars that would be observable in a given survey, we first estimate
the fraction of satellite stars that are likely to be K-giants. Using the Besancon model of the Milky Way(Robin et al. 2003; http://bison.obs-besancon.fr/modele/) we find that 10–15 percent of the halo
population are K-giants. In the Besancon model, the halo is represented by a old, metal-poor stellar
population with an initial mass function dn/dm ∝ m-0.5 (Haywood at el. 1997), which is a reasonable firstorder approximation to the stellar populations expected in the progenitors of halo streams.

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Figure 4-6: Distribution of debris from an inner halo satellite after 12 Gyr as a function of RA and Dec.

The goal is to detect and then characterize those streams we detect; the characterization of a stream
includes a determination of the integrals of motion (e.g., energy E) of the satellite which gave rise to thestream, as well as an estimate of the internal velocity dispersion of the system based on the dispersion in
the integrals of motion among stars in its debris trail. Relatively small numbers of stars suffice to detect
the presence of substructure in velocity space. For example, Helmi & de Zeeuw (2000) show that the twopoint correlation function in velocity space can be used to detect clumping - the presence of substructure
shows up as an excess of stars with small velocity differences. Similarly, streams with favorable energies
and angular momenta can be detected against the Galactic foreground with small samples of threedimensional velocities - Helmi et al. (1999) identified a debris clump containing 13 stars (out of a sampleof 97). We therefore assume that for a robust detection of the phase-space overdensity associated with aparticular satellite, approximately 100 member stars would be required. However, to characterize such a
population requires significantly more data, and in what follows we estimate that around 1000 satellitestars is the minimum required for adequate characterization.
Of course, all stellar debris in the halo will be observed through a foreground of Galactic stars. While
multi-band photometric surveys (e.g., 2MASS, WFCAM, VISTA) can be used to preferentially target Ktype stars, the selection will not be completely efficient. Using the Besancon model, we find that at
Galactic latitude (b ~30º) there are ~4000 stars per WFMOS field which satisfy V < 20 and 2 < V – K <3, as opposed to ~200000 at b ~0º. Thus fields at b >30º should be the primary focus of the search for halostreams. Naturally, this will mean that low-latitude, localized streams will not be detected. However, at
low latitude the number of pointings to achieve complete targeting of K-giant candidates becomesprohibitively large - complete targeting is essential to ensure sufficient numbers of stars per stream will be

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observed. In contrast at high latitude we will supplement the K-star selection with candidate MSTO andHB stars depending on the photometric survey results.
In the following, we restrict ourselves to considering several 800 deg2 surveys to V = 20 with the
Galactic foreground removed via observation of every K-giant candidate (i.e., b>30°). For each survey,
one realization of each satellite is selected and the number of stars falling within the survey region iscounted. It is assumed that 10 per cent of those stars will be K-giants, and that 1000 stars are required tocharacterize the satellite. The survey design considered comprised (a) a 5-degree-wide block (b = [30º,35º], I = 0º to 180º); (b) a cross made up of two 2-degree-wide strips (l= 180º, b=[−90º, 90º]; l=[0º, 180º],b=[30º]); (c) a 10-degree-wide block (b=[30º, 40º], l = 0º to 90º). The results of this analysis revealed that,even through the cross survey structure has the highest chance of ‘clipping’ a stream, the simple block
structure was the most efficient design, allowing the characterization of ~30% of the substantial stellar
streams in the inner halo (with a progenitor mass exceeding 107 M). Reducing the survey area of theblock regions to only 400 deg2 reduces this to ~10–15%.
For the outer halo, streams are more spatially confined as the timescale for phase mixing are much
longer and precession rates are much lower. However, we note that the nature of these streams makes
them the easiest to detect as they are localized on the sky. It is therefore expected that the majority of suchstreams will have been identified prior to WFMOS using all-sky surveys (see e.g., the recent work by
Belokurov et al. 2006). For as yet undiscovered massive streams in the outer halo, a cross-like pattern ispreferred since the surface densities of the targets is sufficiently high for the intersection to yield enoughmembers. We conclude that our survey should not be optimized for the detection of these streams,
although we note that WFMOS will be the ideal instrument with which to obtain follow-up spectra intargeted regions of such streams in an efficient manner.
Table 4-3 summarizes the results of our analysis for block surveys, listing the percentage of massive
7(10 stars) and low-mass (106 stars) streams, which are characterized by block surveys. The 800 square
degree survey also allows detection (100 stars per stream) of 50–100% of outer halo streams and ~90% of
inner halo streams, although as noted above in-plane accretions are systematically missed. In summary,we conclude that an 800 square degree survey with the fields arranged in a 10-degree-wide block at highlatitude (b>30°) will characterize about 30 per cent of all halo streams. We note that this survey could be
supplemented by a low-latitude block to observe streams which were missed by the main survey.
Table 4-3: Summary of results of our analysis for block surveys
Block of 800 square degrees (b ∼30°)
Block of 400 square degrees (b ∼30°)
N = 107N = 106
N = 107N = 106
Inner
Outer
Inner
Outer
Inner
Outer
Inner
Outer
50 – 60%
50 – 100%
30%
50%
60%
50%
10%
25%

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4.2.4 Targeting Substructure; Streams & Dwarfs
To help resolve the CDM satellite crisis, it is vital to uncover the true dark matter content of dwarf
galaxies. In addition the distribution of dark matter within dwarfs will reveal the presence of central coresor cusps, providing a measure of the fundamental nature of dark matter itself. Such measures require
targeted surveys of the known dwarf population. Given their relatively small spatial extent, dwarfs can be
fully targeted using a small number of field pointings using low-resolution measurements to probe theoverall kinematics and abundances of the stellar components. The central regions on the other handprovide a sufficient density of targets for HR followup in order to characterize the detailed chemical
evolution of their, often, complex multi-component nature.
In contrast, streams require more extensive coverage to characterize their systemic velocity variation(and potentially abundance variation) as a function of position and the variation of the velocity dispersion
azimuthally along the stream. Here low resolution surveys are key to quantifying these physical processes
yielding measures that are extremely sensitive to, and hence make excellent probes of, the underlying
Galactic potential and the nature of the dark matter component. Furthermore, the low contrast and large
angular extent of the streams are well matched to the proposed WFMOS field-of-view and multiplexing.

4.2.5 Targeting M31 & M33
As noted previously, M31 and M33 offer the only realistic opportunity to obtain kinematic andmetallicity information on individual stars in large galaxies other than our own. Recent mapping withwide field cameras have provided a detailed source catalogue of stars over one third of the halo of M31(out to a radius of ~100 kpc), with future observations mapping the remaining two thirds. Such acatalogue provides a unique opportunity to obtain the kinematic and chemical information needed tounravel the formation and properties of the stellar and dark matter halo of an external L* galaxy. Hence,
the observing strategy is straightforward, using WFMOS to obtain spectra in LR mode to tile the halo.
The limiting factor, however, is the spectrograph efficiency; assuming an end-to-end efficiency of 15%,
an 8hr integration for an I = 21.5 star (probing 1 magnitude down the red giant branch) will result in a
S/N~5/ Å, sufficient to determine velocities to 5 km/s and [Fe/H] (from the CaT proxy) to an accuracy of
0.2 dex. A panoramic study with WFMOS would require a targeted survey covering a total of ~400 sq
degrees (~2 sq deg/night) for a total of 200 nights. This is impractical and hence, an efficient sampling,based on previously acquired photometric data, is necessary, focusing on a smaller subset of ~25–50
fields will be required. While this is still an ambitious undertaking, we note that the strongest constraint
on the shape of any dark matter halo is the gradient along stellar streams and any sparse tiling strategy has
to be carefully developed to efficiently enable the requisite measurements to be made.

4.2.6 Overall Survey Design
Exposure times and other technical details associated with the GA surveys are presented in Section 6.
Here we summarize the survey details:

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Galactic LR mode; ½ hr integrations, 20/night•

Galactic HR mode; 1 hr integrations, 10/night•

M31/M33 LR mode; 6 hr integrations, 1/night

The ordering in this table represents a priority listing, initially targeting Streams/Dwarfs to obtain
early results. Similarly, the Bulge survey will result in significant early key results, while the completed
LR/HR data will provide resource to be mined over many years. Outer Disk and Halo will produce
several initial results, but key results will come from the completed surveys.
Note: M31/M33 is a proposed future survey to be undertaken after the Primary Galactic Survey. It
will be completed in two stages: Stage 1 targets known substructure and a course sampling of the halo,
while Stage 2 is geared to provide a complete kinematic census of the halo of M31/M33
TargetStreams/Dwarfs

Mode
LR
HR
LR
HR
LR
HR
LR
HR

Bulge
Outer Disk
HaloPrimary Survey
Number of Fields
330
110
300
600
270
100
630
315

Number of Hours
165
110
150
600
135
100
315
315
1890 (190 nights)

Total

M31 & M33

LR

Future Survey
100 (Stage 1)
130 (Stage 2)

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The geometries of the surveys are depicted in Figure 4-7.

Figure 4-7: Aitoff projection of the proposed Primary Galactic Archaeology Survey.

4.3

Technical Considerations
Here we collate those technical considerations which flow down to instrument requirements as well

as data reduction issues specific to the GA surveys.

4.3.1 Spectroscopic Requirements
For detailed HR abundance work most lines of interest are weak e.g., EW <100 mÅ. Ideally theyshould be on the linear part of the curve-of-growth (see Figure 4-8) but this is not always possible so wemust contend with lines in the range 10 mÅ to 250 mÅ. Notable exceptions are the CaT lines used in the
LR analysis, which are generally heavily saturated and on the damped part of the curve-of-growth. Herethe CaT line profiles are dominated by intrinsic broadening due to saturation (typically FWHM 2Å–3Å)and to a lesser extent by the convolution due to the resolution of the spectrograph (at R = 5000 the
spectrograph FWHM ≈ 1.7Å).

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Figure 4-8: The curve-of-growth of typical weak lines highlighting the critical dependence of theabundance determination on knowledge of the microturbulent velocity

Turbulence and rotation velocities of late-type giants typically only broaden the line profiles by a few
km/s, and hence the lines are unresolved at R = 20,000 (FWHM ≈15km/s) and the line profile is generally
dominated by the spectrograph. Early-type stars (and dwarfs) have velocity dispersions much greater than
this (e.g., faster rotation velocities) and hence spectra are fully resolved.
Despite the dampening wings, to first order even the LR CaT lines can be reasonably wellapproximated by Gaussian functions (e.g., see Figure 4-9), and we can use this to gain some insight into
the limiting factors that determine the accuracy of the velocity and EW measurements (cf. Battaglia et al.
2008).

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Figure 4-9: Top panel: the upper portion presents the CaT region for a K-giant in the Sculptor dSph with
derived [Fe/H] = -1.50 dex LR (-1.63 dex HR), showing an example of automated continuum fitting(dashed line). The lower portion presents the residual after continuum removal and after subtracting
Gaussian model fits to the three CaT lines. Lower panel: the derived cross-correlation function andassociated Gaussian fit around the peak region.

Here we define resolution as FWHM = 2.35σ, where σ is the Gaussian profile equivalent scaleparameter (e.g., σv = 2 km/s then the FWHM = 4.7 km/s, i.e., ≈ 0.1Å at 6000Å). Since the total line flux
= I p 2πσ = I p × FW HM × 1.07 , where Ip is the peak flux, this implies that line saturation (i.e., Ip = C)occurs when EW ≈ FWHM. The intrinsic FWHM of weak lines in late-type giants is typically only a few
km/s, i.e., lines with EWs of ≈100 mÅ and above are saturated. As noted previously, the CaT lines areheavily saturated anyway and typically have EWs well above 1 Å.

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To a reasonable approximation, and certainly for weak lines, the noise in the continuum (σn/Å) due tosky plus object dominates, and over any individual line the continuum level/ Å, C, can be taken to beconstant. Therefore, the EW and its error due to random noise, ΔEW, are given byIσw
w
EW = total Δ EW = n
=
C
C
s:n

where the total line flux is Itotal, s:n is signal:noise per Å in the continuum, and w is effective width (Å) theline is integrated over. For a fitted Gaussian profile w = 4πσ ≈ 1.5 FWHM hence
Δ EW =

1 .5 FWHM
1 .5λ / R
=
s:n
s:n

and the FWHM of the line(s) and the continuum signal-to-noise are the primary abundance error drivers
from a measuring point-of-view. 1
In a similar way we can place constraints on the accuracy of measuring velocities. For Gaussian-likeline profiles, which is a good approximation even for native saturated lines like the CaT, the minimum
variance bound on the error in the estimated line position is given byσ 2 σ n2 16πσvar λˆ ={} ηηwhere the line flux is η and where the noise in the continuum, σn (/Å), dominates and can be taken to be aconstant over the region of interest. Rewriting this in terms of the FWHM of the line, the EW and thecontinuum signal-to-noise s : n, leads toΔλˆ ≈FWHM 3/2
EW s:nwhere again all measurements are per Å.
As examples: for LR the two strongest CaT lines, λ8542 Å, λ8662 Å, used in abundance analysis areheavily saturated and have a FWHM at a resolution of R = 5000 of 2 Å –3 Å, while the EW of each line istypically ≈2 Å. For a continuum signal-to-noise of 10–15/ Å this implies a lower bound on the combined
EW error of ≈0.2–0.3 Å, which translates to an error of ±0.1dex in [Fe/H]. This also implies accuracies ofwavelength centering of around 0.1 Å per line, or equivalently ≈2–3 km/s velocity errors using all three
CaT lines. Factoring in the LR Mgb region will then further reduce the achieved velocity to below±2 km/s. This should be achievable in ≈1800s integrations to V = 20.
The last part of the equation on the RHS is only valid if the resolution of the spectrograph dominates the FWHM.
This is not generally the case on the non-linear part of the curve-of-growth, or for early-type stars with high rotation
velocities, or sub-dwarfs with high surface gravity.
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At HR for the weaker lines of interest, EWs to ≈5mÅ accuracy will be a typical requirement. At
R=20000, in many cases the resolution will be dominated by the spectrograph, which suggest that at afiducial 6000 Å with s:n 150/ Å implies a ΔEW = 5mÅ . This should be achievable in ≈3600 s
integrations to V = 17. Line blending is not a serious issue for most lines of interest if standard
multicomponent line profile model fitting is used for EW analysis.
A further requirement on the spectrograph is that scattered light is kept to a minimum sincecontinuum level estimation can be seriously biased by “DC” offsets due to unexpected scattered light.
Accurate stellar atmosphere modeling and subsequent chemical tagging also critically depends on the finedetail of series of EW measurements, any systematic bias in the continuum level is a serious impedimentto accurate analysis. The effect on EW estimation is straightforward. If the continuum level is wrong by
5% due to a scattered light offset, then since the derived line flux, integrated with respect to thecontinuum will still be correct, the EWs will be wrong by the same fraction as the continuum level.
For the low resolution survey, EWs are typically quite large. For example, the sum of the 3 CaT line
EWs is typically within a factor of 2 of about 5Å. An EW error of 10% would therefore translate to asummed EW error of 0.5Å which translates to a systematic error of 0.2 dex in derived [Fe/H]. We areaiming here for 0.1 dex rms errors so would certainly expect unpredictable systematic errors to be nolarger than this. This would suggest that for LR the unpredictable scattered light component should be
below 5% of the continuum level.
For the high resolution work, most of the lines of interest are in the range 10mÅ to 100mÅ, withsome up to 250mÅ. Similar arguments hold: systematic errors should be no worse than the 5mÅ rms
accuracy from s:n considerations. This leads to the same 5% accuracy requirement for the high resolution
survey.
Accurate fluxing of spectra is also a requirement since comparison with external galaxies over a rangeof redshift, via spectral synthesis models, depends on accurate calibration of line indices. Likewise
linking the high redshift universe with detailed comparison of the Galactic Bulge components depends on
good spectrophotometric calibration. Regular observations of flux standards should suffice to define thedifferential flux calibration, whilst fiber, slit and other losses can be monitored and corrected by directcomparison with broadband photometric data. A relative flux calibration of better than 5% will suffice
and is typical of the accuracy of external galaxy spectrophotometry.
We summarize our discussion of the instrument requirements for the GA survey in the following
table:

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Table 4-4: Galactic Archaeology Spectroscopic Requirements SummaryHigh Resolution mode
R=20,000
V=17
S/N = 10-15 / Å
Low Resolution mode
R=5,000
V=20
S/N = 150 / Å
Wavelength accuracy:
0.1 ÅContinuum estimation accuracy:
5%
Relative flux calibration accuracy:
5%

4.3.2 Data Reduction Requirements
Automated LR data analysis, particularly in the CaT region, is well advanced, but it is still worth
applying information theory techniques in this wavelength regime in conjunction with the Mgb region tosee if we can extract more information other than velocities and proxy [Fe/H] measurements (e.g., Munari
et al. 2005). With a range of different species present in both wavelength ranges, albeit often as weaklines, several studies are currently underway in addition to our own efforts (e.g., Kirby et al. 2008) toascertain if reliable global abundance ratios such as α-element ratios can be derived from LR spectra.
Automated HR data analysis presents a more complex challenge, although several promising lines ofdevelopment are being investigated by members of our team. The first challenge is to combine
photometric measurements, distance estimates and kinematics, with global spectral properties to derive
reliable stellar atmosphere parameters (log g, effective temperature, microturbulent velocity, overall
metallicity and α-element ratio) prior to undertaking detailed abundance estimates of individual species.
A key area under investigation by our team is the use of new methods such as Artificial Neural Nets
(ANNs), Self Organizing Maps (SoMs), or Principal/Independent Component Analysis (PCA/ICA).
These may eventually improve upon traditional feature extraction approaches based on line lists (e.g.,
direct EW measurement) combined with model atmosphere template fitting (cf. Kurucz model
atmospheres as in Munari’s extensive library, or alternative stellar model atmospheres such as MARCS
e.g., Gustafsson, et al. 2008).
Standard data processing can build on existing tools for basic instrumental signature removal,
bookkeeping, spectral extraction and wavelength calibration (e.g., 2dF, 6dF, WYFFOS, FLAMES....).
Specific additional issues to be dealt with include:•

Data formats, FITS file structure and HDU (Header Data Unit) content - all data structures shouldhave sufficient meta-data present to be self-describing by, for example, also including fiber
allocation table as FITS table extensions•

Instrument stability and calibration frequency - including flats, arcs, standard star observations
both for flux calibration and velocity verification. Accurate, robust and stable wavelength
calibration is a critical requirement for good sky subtraction (i.e., at least 1/20th of a resolution
element accuracy over the entire wavelength range of interest is required).

•Automating sky subtraction in an efficient way is critical to the success of the both the GA andDE surveys - this is discussed further in Section 6

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It is crucial to benchmark data reduction (and analysis) strategies for testing data reduction
techniques on realistic data sets. In this regard our active involvement in several VLT FLAMES
surveys provides us with an excellent set of real benchmarking data that is being analyzed bothwith conventional methods and prototype analysis tools required for full GA data analysis.

•

Data fidelity will be ensured to comply with Virtual Observatory standards for archiving andfuture dissemination.

An important aspect of the WFMOS GA survey will be the implementation of a complete data
reduction pipeline that takes raw data to physical parameters efficiently, with as little user intervention as
possible. Similar approaches are being adopted for ongoing stellar surveys (e.g., MATISSE; RecioBlanco et al. 2006, SEQUE; Lee et al 2008a,b, Prieto et al 2008; Shetrone et al 2008; RAVE; Zwitter et al2008). These automated methods are vital to the efficacy of large stellar surveys, contrasting with thehistorical reduction of high resolution stellar data which has been typically manual and extremely time
consuming. The following sections outline our studies to date, but our development efforts will build on
the successes (and note the difficulties) of ongoing projects. (Issues relating to sky subtraction, which arecommon to all WFMOS surveys, are dealt with separately in a later section.)

Figure 4-10: Example of automated continuum estimation and normalization using FLAMES
benchmark HR test data.

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4.3.3 Continuum Estimation
In a similar manner to the methods devised for optimitsing sky subtraction, iterative techniques forautomating continuum estimation at both LR and HR exist (cf. Figure 4-10) and can be used to prenormalize the spectra prior to more detailed analysis. Each spectrum is split into a line-only, and smoothly
varying continuum-only component, using an iterative k-sigma clipped non-linear filter. Regions aroundselected strong lines, e.g., CaT or Mgb lines, can be optionally masked out, prior to filtering, to preventthe continuum tracking the wings of strong absorption features. The effective scale length of the filteringis here typically set to around 15Å which, in conjunction with the masking and iterative clipping, is fineenough to follow continuum trends without being affected by the presence of other strong lines. At thesame time an estimate of the overall signal-to-noise in the continuum is made by measuring the mediancontinuum level in the analysis region and by measuring the pixel-to-pixel noise covariance matrix inregions containing no lines. The latter is needed to correct the apparent random noise for the cumulativesmoothing effects of spectral interpolation and resampling, which we find typically results in a factor of≈2 overall random noise reduction, and concomitant underestimation of derived parameter errors if notaccounted for.

4.3.4 Velocity and EW Estimation
After normalizing by the computed continuum velocities are estimated by cross-correlating each
spectrum with a model function template. We illustrate this for the CaT region where the template can be
simply constructed using a zero-level continuum with three Gaussian absorption lines located at thevacuum rest wavelengths of the CaT lines superimposed. 2 Here the Gaussian line depths are scaled in theratio 3:5:4 to reflect the different strengths of real CaT lines and all are set to have a full width at half
maximum (FWHM) = 2.35Å. An example of a continuum fitting to a Sculptor dSph K-giant spectrum
together with the computed cross-correlation function and Gaussian fit to the peak is shown in Figure 4-9.
As an alternative we have also used stellar model atmosphere spectra to provide excellent matching
templates for velocity estimation.
An accurate estimate of the position of the cross-correlation peak is made by fitting a Gaussian to a
localized region around the peak. This velocity is then used to define the wavelength region around each
CaT line to use for EW estimation. After some trial and error we settled on a region 15 Å wide centeredon each line as a reasonable tradeoff between including all the line flux and minimizing the noise in thesimple integral estimator. In addition to the straightforward sum we also fit individual unconstrained
Gaussian functions to each CaT line over the same wavelength region and use the derived flux as analternative EW estimator. This also allows a semi-independent check on the accuracy of the derived
Most of the weight in the least square fit comes from the core of the lines which are sufficiently
Gaussian-like that a Gaussian fit provides an estimate close to the minimal rms error bound. Using more
complex line profiles with correspondingly greater numbers of free parameters generally makes the rms
error worse and is also more prone to wildly unstable solutions due to the inevitable presence ofoccasional artifacts in the data.

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velocity by providing three separate velocity measures with associated errors. The weighted sum of thesevelocity errors provides the basic velocity error estimate for the velocity derived from the crosscorrelation. We prefer to use the latter for the actual velocity estimate since it is effectively a constrainedmodel fit. As a final step the derived velocity is corrected to a helio-centric system.
The combined equivalent widths for CaT lines #2 and #3 for both the integral and Gaussian fit arethen compared and used to compute an overall correction to the Gaussian version. This is necessary since
the real line profile is a complex function of many parameters, and in particular, the dampening wings
visible in strong lines are distinctly non-Gaussian in appearance. This correction is equivalent tocomputing the average overlap integral between the real line profiles and the Gaussian fits and isaccomplished by measuring the gradient between the two as a function of EW. We prefer to use theGaussian-derived EW estimator since although the simple integral is unbiased it is also significantly
noisier than a Gaussian fit.

4.3.5 Stellar Atmosphere Modelling
The general stellar atmospheric parameters that need to be estimated are: effective temperature Teff,
overall metallicity [m/H], systemic velocity V, microturbulent velocity Vturb and surface gravity log g.
By combining detailed broadband photometric measurements ranging from optical through near-infrared,
with distance estimates and kinematic measurements, with global spectra properties we anticipate being
able to quantify these parameters to the necessary precision to enable detailed abundance measurements
of individual line species.
The most promising way forward involves extraction via line lists and direct EW measurements,
combined with Maximum Likelihood model atmosphere template fitting using both Kurucz model
atmospheres, as in Munari’s extensive library or alternative stellar model atmospheres such as MARCS(Gustafsson et al. 2008). Additionally, we are exploring PCA/ICA decomposition methods to focus in onalpha-sensitive features in both LR and HR spectra, in conjunction with Maximum Likelihood model
fitting to attempt automatic overall [α/Fe] estimation. An example of a model atmosphere fit is shown in
Figure 4-11.

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Figure 4-11: The best fitting continuum-subtracted model spectra for solar and alpha-enhanced
abundance ratios compared to an HR VLT-FLAMES R=20000 continuum-subtracted spectrum with S:N~100/A. Upper panel: continuum-normalized data; lower panel: top is alpha-enhanced model spectrum(black); middle offset solar ratio model spectrum (green); the mask function used in the fit is in blue.
The solar abundance ratio spectrum gives a marginally better fit.

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4.3.6 Chemical Tagging

Figure 4-12: Example of chemical tagging for the solar spectrum

Stellar atmosphere modeling will result in a complete decomposition of the physical properties of thestar and its chemical fingerprint (e.g., Figure 4-12). While a complete description of the chemical
evolution within the Galactic environment is beyond this document, elements can be grouped in terms of
their underlying physical processes, where the main categorization will be along the lines of thefollowing:•

Light elements (e.g., C,N,O,Na,Mg,Al) are tracers of deep mixing patterns, found only inglobular cluster (GC) environments, this gives a limit to the number of dissolved GCs in a stellar
population. Evidence for differences in these ratios (to Fe) between field pop II giants and GC
giants is shown, for example, in Pilachowski, Sneden and Kraft (1996); and Shetrone (1996).

•

Alpha elements (e.g., O, Mg, Si, Ca, Ti) occur predominantly in SNeII,. The abundance ofdifferent alpha elements is sensitive to the mass of the SNeII progenitor, even Z alpha abundances
limits the number of SNeII explosions and hence constrains the IMF /ejecta fractions with time•

Heavy elements (Z > 30) (e.g., Y, Ba, Ce, Sm, Eu) come from a mix of r- and s-processes i.e.,
those beyond the iron peak formed by repeated neutron capture•

Rapid capture or r-process, occurs in high energy events such as in SNe explosions, e.g., Eu isthought to be produced almost entirely by the r-process•

Slow capture or s-process, probably in more quiescent situations, e.g., from stellar winds during
AGB phase typical elements are Ba and La e.g., Ba/Eu is a measure of the contribution of AGB
stars (note can be anomalous via self pollution but this causes large Ba/Fe ratio)•

Iron peak elements (V, Cr, Mn, Co, Ni, Cu, Zn) come mainly from SNe1a, i.e., older binary
systems so ratio of alpha to Fe gives leverage on SFH while Zn is a good tie in with high redshift83

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studies - also elements heavier than about Zn are predicted NOT to exist in zero metallicity stars or those enriched purely by very massive first stars (especially pair production SN) another sign
to look for. Cu is supposedly produced almost solely in SNe1a
Note that the chemical decomposition will result in a high-dimensional parameter space and theapplication of clustering algorithms within this volume will be used to isolate populations with strongly
similar chemical properties (and hence possibly from the same proto-stellar cloud); such identification isrequired to piece together the historical chemical evolution of the Galaxy. It is important to note thattypical errors are 0.1 dex and the spread may only be 0.5 dex max. So this limits the likely detective workto ≈3–5 divisions in each of the above tracer groups and even here many of these characteristics may becorrelated. However, given so many different sites for chemical evolution the parameter space is stillenormous and offers the potential to tag individual groups even in such complex regions as the Galactic
Bulge (c.f. McWilliam 1997 and Tolstoy et al. 2003).

4.4

Competition
Although there is no direct competition with the same facility scope in terms of survey depth,

spectroscopic wavelength coverage and resolution, there are several facilities either being used to conduct
surveys or that will, or may, be available for GA-like surveys over the next 10-year period. We give a
brief list of the main protagonists below to set the proposed WFMOS GA surveys in context.
•

RAVE—is a southern hemisphere multi-fiber (150) survey of approximately 250,000 stars, with apossible extension to 1 million stars, at relatively bright magnitudes, 9 < I < 12, being conductedon the 1.2-m UK Schmidt telescope in Australia (Steinmetz et al. 2006). The large field-of-view(6-degree diameter) and small aperture constrain the survey to bright limits and hence relatively
nearby (5–10 kpc) stellar populations. The wavelength range, 8430–8740 Å, and resolution,
R=8500, are similar to the planned Gaia spectroscopic survey. The survey began in 2003 and isdesigned to run for 5–10 years depending on final scope.

•

Keck DEIMOS—although DEIMOS is a general purpose multi-slit (120) spectrograph it has beenused for studies of nearby resolved stellar populations by several groups, mainly in the CaT
region with a wavelength coverage of 2500 Å at a resolution of R = 6000. Its main advantages are
the high overall throughput, ≈29%, 10-m aperture and reasonable field-of-view, 81.5 arcmin2.
This is well matched to studying nearby satellite systems such as small dwarf galaxies andglobular clusters around the Milky Way and M31 (e.g., Simon & Geha 2007) and has been
extensively used by two groups to probe the kinematics and overall metallicity of substructure in
and around M31 and M33 (e.g., Ibata et al. 2005; Kalirai et al. 2006). Although the field-of-view
is impressive for a 10-m-class telescope it is nowhere near large enough to efficiently map thestellar populations of either the Milky Way or the general M31 system, and it does not support a
multi-object high-resolution mode.

•

BRAVA – The Bulge Radial Velocity Assay program is being undertaken on the 4-m CTIOHYDRA, using 138 fibres to target 2MASS-selected M-giants in the Bulge at R~2800, with thegoal of mapping the kinematic structure of the Bulge and Bar. The current survey focuses on a84

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strip along b~-4o, with a vertical extension at l = 0o, each field covering 40 arcmins. With morethan 3300 stellar velocities to date, this survey has published its initial results (Howard et al.
2008).
•

FLAMES—is a multi-object fiber (130) spectrograph on one of the ESO VLTs with a 30-arcmin
diameter field-of-view. In GIRAFFE mode it offers a similar set of resolution options in LR mode
(R≈6000) and HR mode (R≈20000), to those proposed here for WFMOS. However, the limited
wavelength coverage per setting, particularly in HR (≈250 Å), together with the wider field-ofview and number of fibers, gives WFMOS a 20–50 multiplex gain over FLAMES. As with Keckthis setup has been used for a variety of studies of resolved nearby stellar populations with two of
the ESO Large Programmes of particular note (e.g., Tolstoy et al. 2004; Koch et al. 2006). In
some respects this is acting as a pathfinder system for trialling the approach we are proposing for
the GA case.

•

Magellan MIKE—is a double echelle spectrograph that gives simultaneous coverage in the blue
(3200–4800 Å) and red (4400–10000 Å) wavelength regimes at resolutions of 19000 and 25000,
respectively, over a 30-arcmin diameter field. In multi-fiber mode up to 256 objects can be
simultaneously observed if suitable order blocking filters are used to select specific wavelengthintervals (typically ≈200 Å wide). It has been extensively used to study stellar populations insouthern Milky Way satellites (e.g., Walker et al. 2007). IMACS, a lower resolution imaging
spectrograph, appears to have been mainly used for deep extragalactic projects, though inprinciple could be used for LR nearby stellar population studies. The limitations of both
spectrographs relative to WFMOS are similar to FLAMES.

•

AAOmega—on the 3.9-m AAT has the benefit of a 2-degree field-of-view and has two
spectrographs fed from 392 fibers giving selected simultaneous blue and red wavelength coveragein the range 3700–9500 Å. For stellar studies the relevant comparison is with the R=8000–10000
gratings which allow simultaneous coverage of the Mgb (5100–5450 Å) and CaT (8450–8850 Å)regions. A modest survey of the Galactic Bulge region of some 20000 stars with V < 18 begun in2008 is making a start on a chemo-dynamic analysis of the central regions of the Milky Way.

•

SDSS/SEGUE I/II—primarily designed for galaxy redshift surveys, the 2.5-m SLOAN survey
telescope has already been used for a sparse sampled stellar spectroscopic survey over large
patches of (mainly) high galactic latitude sky. Although providing fascinating insights into thebulk distribution of Galactic components (e.g., Allende Prieto et al. 2006), the low resolution,
R=2000 and the small telescope aperture preclude detailed investigation of stellar
subcomponents. Future planned Galactic surveys include extending SEGUE I-II and an APOGEE
H-band Bulge survey from 2009.

•

LAMOST—is 4000-fiber spectroscopic survey instrument housed on a 4-m Schmidt telescopewith a five-degree field-of-view (first light October 2008). It is expected to begin surveys during
2009/10/ and one of its main aims is to carry out a large area spectroscopic survey of the MilkyWay. In survey mode the resolution will be relatively low, R=1000–2000, and blue and red arms
will give a wavelength coverage of 3700–5900 Å and 5700–9000 Å. The LR survey
spectrographs have been optimized for galaxy redshift surveys and although it will compete
effectively with the SDSS stellar surveys, again it will not have high enough survey resolution tocarry out the main GA WFMOS programs.

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Gaia—the RVS component of Gaia will conduct an all-sky V < 15 survey from 2011/12 and thenfrom a full combination of scans by 2020 the aim is to attain V < 17. The wavelength coverage
will be limited to the CaT region, in this case 8450–8750 Å, at a resolution of R=10000. As noted
earlier this and the astrometric goals of Gaia complement the proposed WFMOS LR surveys forkinematics and overall metallicity measures, while the WFMOS HR survey would provide
detailed abundance analysis.

•

HRMES—is a proposed 392-fiber R = 30000 spectrograph to be deployed on the 2DF positioner
on the AAT. It is being designed to cover two 150-Å wide bands simultaneously and aims forfirst light in 2012. One of its primary science drivers is HR studies of nearby stellar populations.

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5GALAXY FORMATION AND LARGE SCALE STRUCTURE

5.1

Introduction
As introduced in §1.2, WFMOS has enormous potential beyond the original two DE/GA campaigns

envisaged by Gemini Aspen process. Following presentations made at two recent conferences, it is clearthat Japanese astronomers are excited by utilizing this instrument for many additional surveys.
Establishing early synergy between the science potential envisaged by Japanese astronomers and our
proposed design will enhance the likelihood that WFMOS is enthusiastically accepted within the broader
Japanese community.
The earlier WFMOS Feasibility Study already discussed the broader potential of the Dark Energy
survey, considering `value added science’ that would be possible exploiting the large z ~1 and z ~3spectroscopic surveys for non-cosmological applications. In this section, we take the discussion further,
however. Here we discuss additional surveys that might be undertaken with WFMOS, including some ofthose presented at the recent Cosmology: Near and Far – Science with WFMOS conference at Kona HI(May 2008). The key issue we wish to address is the extent to which these are compatable with thescience requirements defined by the DE and GA surveys.
In compiling this brief review of additional surveys, we acknowledge useful input from a number ofJapanese astronomers (M. Ouchi (convenor), T. Yamada, T. Kodama, M. Tanaka and K. Shimasaku).

5.2

Strategic Issues
By design the now-funded panoramic imager HyperSuprimeCam (HSC) has same field of view as

WFMOS. Synergy between target imaging and subsequent spectroscopic follow-up is long-standing inastronomy and extends well beyond DE/GA applications. To date, it has been difficult to fully exploit theremarkable imaging capabilities of the existing Suprime-Cam imager because its 30 arcmin field is larger
than the spectroscopic facilities available to the Japanese (e.g., FOCAS, GMOS, DEIMOS). This has
certainly limited the potential discovery space of Subaru. The unique combination of HSC+WFMOS will
rectify this problem over a wide range of science applications. As plans for the use of HSC materialize it
is clear that this imager will undertake deep surveys other than just ones concerned with weak lensing
discussed in §3.6; exploiting these with WFMOS will be an essential application.
There is an explosion of international interest in conducting surveys spanning areas of ~10 deg2 or
greater. These include ultradeep imaging over moderate fields with 4 m telescopes (UKIDSS, VISTA)
and ambitious campaigns with Spitzer, Herschel and future non-optical/IR facilities. We are seeing a
revolution in multi-wavelength applications in astronomy and it is guaranteed that WFMOS will be an
unique asset in this territory.

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5.3

Sample Science Programs
Extensive redshift surveys have already been undertaken with 8-10 meter class telescopes. These

include the color-selected Keck DEEP2 survey of ~40,000 galaxies with 0.5 < z < 1.3 limited at R<24.1,
and the VLT VVDS survey which aims to survey 150,000 galaxies to I = 24. These are both multi-slit
surveys offering better sky subtraction than WFMOS is likely to achieve with fibers and providing limited
resolved data, e.g., for galaxy rotation curves.
The key gain of WFMOS, in concert with HSC, with respect to these earlier surveys will be to spanmuch larger areas for rarer sources and perhaps to offer higher signal to noise for larger samples ofbrighter sources. Cosmic variance continues to plague statistical studies of galaxy evolution and WFMOS
offers the potential of efficiently eliminating this uncertainty. Table 5-1 provides a useful overview of thepotential applications of WFMOS beyond the confines of the earlier DE/GA applications.
Survey Area
1 deg210 deg2
100 deg21000 deg2

Table 5-1: The Range of Survey Areas and Potential Applications
Comoving Volume
Main Targets(Mpc3/unit z) 1<z<10~107
Galaxy Evolution
8~10
The most luminous objects, clusters large scale structure~109
QSOs, cosmic web
10~10
Dark Energy Survey

It should also be remembered that surveys can be combined very effectively so that e.g., an imaging
survey for WL and spectroscopic one for DE can, with minimal difficulty, encompass other applications
and simultaneously cover (rarer) targets. Although some of the sources involved are faint, many haveemission lines and stacking statistical samples may be powerful if limitations arising from sky subtraction
can be addressed.
The following will serve as an illustration of the possibilities. They demonstrate that the wavelength
range and resolution proposed for the GA/DE surveys is compatible with these additional applications.
The programs discussed below are intended to sample the nested hierarchy of field areas listed in Table
5-1. We do not cost the execution of these surveys in our proposal.

5.3.1 A High Quality Spectroscopic Survey to z ~ 1.6
As the primary baryon oscillation survey at z ~1 (§3.7.2) is optimized for emission line galaxies with[O II] detections, the continuum signal/noise in a typical spectrum will be very low. The goal of thatsurvey is to measure the redshifts of millions of galaxies in the most efficient manner possible. Thus,
despite claims in the Feasibility Study, we believe it is unlikely that the main DE survey will generate
spectroscopic data that is adequate for precision studies of galaxy properties (stellar populations, ages,
metallicities etc).
An interesting proposal, therefore, is to exploit WFMOS to secure, for a much smaller area than thatstudied by the DE campaign, higher quality spectra for the purpose of more detailed galaxy studies

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beyond z ~1. Such a mini-survey would complement FMOS Hα emission line data secured in the same
selected areas.
We have developed tools for predicting model WFMOS spectra as a function of the exponentiallydeclining star formation rate timescale τ and the metallicity Z. This allows us to evaluate the increased
signal/noise that would be necessary to recover these valuable parameters. Figure 5-1 provides anillustration of this for a red I~22 z~0.7 galaxy observed in the DE survey and that with a longer exposure
achieving a continuum signal/noise of ~10.

Figure 5-1: Recovering the star formation timescale (τ) and metallicity Z for a red I~22 z~0.7 galaxy.
The top panel refers to a low signal/noise spectrum such as that achieved for a dark energy survey ofluminous red galaxies; the data is sufficient to secure the redshift but little more. The bottom panel
refers to a much higher signal/noise spectrum such as would be possible with longer integrations onbrighter objects (R ~ 21) over a wide area. In conjunction with an earlier survey by FMOS, it is
proposed WFMOS could secure robust stellar properties for a large sample of bright galaxies over
0.7<z<1.6.

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5.3.2 Studies of z > 3 galaxies
Subaru has pioneered the statistical study of both Lyman break galaxies (LBGs) and Lyman alphaemitters (LAEs) beyond a redshift 3. It is clear the HSC will significantly advance this area and WFMOS
can complement the relevant imaging campaigns by providing confirmatory spectroscopy and emission
line statistics. As a key issue will be the depths achievable with a multi-fiber instrument; it is likely thatmost applications will be restricted to sources with strong Lyman α emission.
The surface density of luminous LBGs with I<24 varies from 1400 deg-2 at z ~3 to ~100 deg-2 at z ~5.
Photometric and narrow-band selection has enabled the mapping of LBGs and LAEs in a few selectedareas over 200 Mpc scales (Figure 5-2) enabling their clustering and inter-relationship with other sources(e.g., sub-mm galaxies) to be determined. For the subset with strong emission lines, WFMOS would
significantly enhance such surveys over very much wider fields.

Figure 5-2: The spatial distribution Lyman alpha emitters in the well-studied SSA22 field (preliminary
results from the ongoing survey, courtesy of Yamada et al). WFMOS, working in concert with HSC,
would enable a huge increase in survey mapping speed and provide emission line statistics for yetlarger samples.

WFMOS spectroscopy will yield the equivalent width of Ly α for both LBGs and LAEs and this canact as a valuable tracer of young stellar populations. By stacking spectra for selected higher redshiftsources, the rare He II tracers of Population III stars can be sought. Line profile measures will also
become feasible with WFMOS for the most luminous examples. Stacking spectra will increase thepossibilities in redshift ranges where sky subtraction is straightforward, providing valuable insight into
outflows and the nature of the local IGM (Figure 5-3).

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Figure 5-3: Stacked Lyman alpha line profiles for emitters located from Suprime-Cam narrow band
imaging (from the ongoing survey by Yamada and co-workers). Line widths and profile shapes
constrain outflow models and the nature of the local intergalactic medium.

5.3.3 QSOs, Clusters, and Other Sources
The unrivaled field of WFMOS means that very rare sources such as luminous QSOs will still beabundant within a given pointing and thus correlations between intervening absorbers and Lyman breakgalaxies will be possible over large scales. This is a key motive for a wide field spectrograph on the next
generation of extremely large telescopes, but an early application with less dense sampling with WFMOS
will be highly instructive.
Intermediate redshift clusters of galaxies are likewise natural targets for WFMOS. It is now thoughtthat infalling field galaxies suffer changes to their star formation rates, morphologies and halo properties
as they become influenced by the intracluster gas and the cluster potential. Spectroscopy is crucial for
membership and post-burst age diagnostics. The panoramic field of WFMOS will accelerate progress
dramatically in this area (Figure 5-4).

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Figure 5-4: Panoramic spectroscopy of clusters of galaxies at intermediate redshift (courtesy of
Tanaka & Kodama and collaborators). The turn-around region for galaxies falling into a rich z~0.5
cluster corresponds to a diameter of ~15–20 Mpc and fits neatly within a single WFMOS pointing.
Detailed spectroscopy provides velocities and star formation diagnostics for thousands of members per
cluster. This will enable a variety of studies which attempt to understand how the cluster environment
both enhances and truncates star formation and rearranges the galaxy morphologies.

5.3.4 Implications for Instrument Design
The above programs illustrate the broader potential of WFMOS beyond the confines of the more
substantial DE and GA surveys. It is interesting to ask whether they can be conducted efficiently with thesame requirements.
Fiber aperture: (currently 1.2 arcsec) this is regarded as adequate for a z < 1.6 redshift survey, thecluster and QSO applications. Although smaller fiber apertures would be desirable for the high z LBG
and LAE applications, good progress is still possible with the current fiber aperture.
Wavelength coverage: (currently 4200–9700 Å). The red extension required for the z~1 DE survey

is highly advantageous for the Japanese; it maximizes synergy with the red sensitivity of HSC andensures Ly α is visible to the highest redshifts practical.
Spectral resolution: (currently 3500–4000 across the full range, 20,000 in two selected regions, 5000

around Ca triplet). This is adequate for all the applications discussed above.

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6

TECHNICAL DETAILS

6.1

Introduction
In this section we collate a variety of technical materials that would have interrupted the flow ofscience discussion in Sections 3–5. The discussion includes important issues such as the required imaging
data and photometric methods to be used for target selection for the DE surveys, simulations of sky
subtraction without ‘nod and shuffle’, fiber allocation efficiencies and how these impinge on survey
strategies, and exposure time calculations which govern the eventual Subaru observing time requests.

6.2

Target Catalogs – Dark Energy Surveys
First we outline the planned and ongoing imaging surveys that will become available for target

selection for the DE campaign. We then define a plan of how target selection should proceed given ourcurrent knowledge of these surveys and their data policies. The relevant surveys include CFHTLS-Wide,
KIDS, Dark Energy Survey (DES), Pan-STARRS1 and HyperSuprimeCam (HSC) surveys. Where it is
known, the sky coverage of these is summarized in Figure 6-1.

Figure 6-1: Sky coverage for DES (blue, 5000 deg2 ), KIDS (red, 1500 deg2) and CFHTLS-Wide (green,
150 deg2). Pan-STARRS (omitted for clarity) will map 3π steradians seen from Hawaii. HSC coverage is
not yet decided.

We first give a brief description of each survey:•

CFHTLS-Wide (the Canada-France-Hawaii Telescope Legacy Survey Wide): This survey isongoing and is planned to cover 150 deg2 in ugriz over four different fields. The CHFTLS-Widesurvey should be completed in the next few years and has already a considerable amount of data
publicly available. Contrary to most planned surveys the CFHTLS has a considerable depth in the
bluer filters including a u band photometry planned to a depth of 26.4 AB.

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT•KIDS (the Kilo-Degree Survey): This will cover 1500 deg2 in ugri and 2800 deg2 in z by utilizing
the VST/OmegaCAM in Chile. KIDS will cover two regions on the sky: an equatorial strip on the
North Galactic Cap and a patch around the South Galactic Pole. The same area will also becovered by VISTA in JHKs. The expected magnitude limits are given in Table 6-1. The VST has
seen major delays in its commissioning, but survey operations are now expected at the end of
2009 (Bender, private communication).

•

DES (the Dark Energy Survey): This international collaboration (US-UK-Spain-Brazil) will use
the Blanco 4m telescope in Chile. It will image the sky with five filters: griz and Y bands. The
instrument will be used down to i = 24 (see Table 6-1) over approximately 5000 deg2 of theSouthern sky during 2011-2016. In addition VISTA Hemisphere Survey will map the same area
as DES in J, H and Ks. This will provide significant improvement in the derived photometricredshifts for 1.3<z < 2 (e.g., Banerji et al. 2008).

•

Pan-STARRS: The Pan-STARRS observatory in Hawaii will have four 1.8 m telescopes, withgrizy and w filters (but not u). The optics delivers a 3 deg field of view. The first telescope of
Pan-STARRS achieved first light in 2007 and has undergone an extensive commissioning processduring 2008. The camera and telescope have passed a formal Operations Readiness Review, andfull survey operations are expected from April 2009. Pan-STARRS1 will pursue a 3π survey ofthe sky observable at Hawaii during the following 3.5 years.

•

HyperSuprimeCam (HSC): With its field of view matching the WFMOS filed of view, and aplanned survey, which will precede the WFMOS surveys, it is also the only camera located on an
8m aperture appropriate for target selection. Although the survey is not yet confirmed it is likely
to be 2000 deg2 to depths listed in Table 6-1.

We note that the latitude of Hawaii (the site of Subaru) is +18°, while the imaging surveys will betaken from either Hawaii (Pan-Starrs or Subaru) or from Chile (KIDS or the Dark Energy Survey), at
latitude of about –30°. Clearly a considerable fraction of the surveys obtained from telescopes in Chile
will not be observable by Subaru.
Table 6-1: Planned imaging surveys: AB magnitude limits per SDSS-like filter. Depths are taken fromvarious survey websites. HSC depths follow a private communication (Takada)
Band
DES
KIDS
PanStarrs1
HSC
CFHTLS
u
NA
24.1
NA
NA
26.4
g
24.6
24.6
24.35
26.5
26.6
r
24.1
24.4
23.85
26.4
25.9
i
24.3
23.4
23.05
25.8
25.5
z
23.9
22.4
22.45
24.9
24.8
Y
21.5
NA
21
23.7
NA

6.3

Strategy for Target Selection
The aim in the BAO survey at z~1 is to concentrate on [OII] emitting objects at 0.7 < z < 1.6 over a

3200 deg2 area. The minimum photometric selection should thus be sufficient to locate the galaxy in a
broad redshift bin. This problem was effectively solved using only BRI photometry (BAB<24 RAB<23 andIAB<25) in the DEEP2 survey for galaxies with RAB<24. As outlined in Section 3, as we are selecting
galaxies with RAB < 23.0, the depth requirements should be less demanding than for the DEEP2 survey by
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roughly a magnitude. We show a simulation of this selection Figure 6.2 where galaxies can be clearly
separated into 2 broad redshift bins above and below z ~0.7.
Each of the surveys discussed above will yield a photometric redshift accuracy superior to thatdefined by BRI photometry, as is illustrated by the photo-z precision of the DES survey shown in Figure
6.2. Therefore, for the low redshift DE survey the key issue is whether the imaging is located in a suitablepart of the sky with sufficient area, and whether the data is available to the WFMOS team.
Our z~1 DE survey requires 3200 deg2 observable from Subaru with suitable imaging. We
conclude that 2000 deg2 of imaging required be taken from the HSC survey which will be completed
previous to the WFMOS survey. An additional 1200 deg2 would be equatorial and taken from the3π survey produced with Pan-STARRS.

Figure 6-2: (Left): Photometric selection from the DEEP2 survey: red and blue points represent
galaxies above and below a redshift of 0.7 respectively, based on BRI photometry alone. (Right):
Simulation illustrating substantially improved photometric redshifts expected for the Dark Energy
Survey (grizY filters) combined with the VISTA Hemisphere Survey (JHK filters). Deeper data such asthat possible with HyperSuprimeCam would further improve the accuracy.

The high redshift DE survey will target Lyman break galaxies with 2.5 < z < 3.3. In this case a
Steidel-like selection (RAB< 24.5 GAB< 25.5 UAB< 26.5) is required. Most planned surveys (DES, PanSTARRS, KIDS and HSC) will not deliver data as deep as required in the ultraviolet. If one were toobtain the U band data directly from HSC it would take a further 15-20 clear nights of survey time fortarget selection over 100 deg2. Fortunately, the CFHTLS-Wide has obtained significant (~150 deg2)
imaging with u band depth suitable for the z ~3 target selection (u'=26.4, S/N=5, 1.5 arcsec aperture).
We recommend that 100 deg2 of ultraviolet imaging required for target selection for the highredshift survey be taken from the CFHT-Wide survey which is partially completed and will befinished before the WFMOS survey starts. The longer wavelength data will be derived from the
HSC weak lensing surveys. A further 15 to 20 clear nights would be needed if the required
ultraviolet imaging were done at Subaru.

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Clearly, we would prefer Subaru HSC imaging datasets as the material for WFMOS targets. This is
still possible in principle; although, our z~1 survey requires over 3000 deg2 of coverage, which willprobably exceed the expected 2000 deg2 weak lensing survey field area. Among the other datasets
described in this section to augment the material, KIDS is an ESO public survey. Similarly the raw DES
data should become public after a year of being taken. We have team members who have access to boththe DES and Pan-STARRS survey data. Via future agreements these imaging data should be available tothe WFMOS team. Some of the CFHT U band data required for the z~3 survey are already available tothe community, and we can expect the full 150 deg2 should be available prior to the commencement of a
WFMOS survey.

6.4

Predicting Emission Lines from Broad-Band Photometry
Although a multicolor (e.g., BRI) photometry cut is effective in selecting galaxies higher than redshift

0.7 for our z~1 DE survey, a further selection in this (or another) color plane can yield samples of blue
galaxies with prominent emission lines. We have investigated how to improve this selection by applying
non-linear statistical methods to the ensemble of DEEP2 galaxies.
Provided with magnitudes (BRI in the case of DEEP2, or ugriz magnitudes if available), magnitude
errors and [O II] EWs for a subsample of galaxies, we have shown that it is possible to improve theselection by using a non-linear correlator such as a neural network (cf. ANNz; Collister & Lahav 2004)
which can usefully predict the emission line strengths (Figure 6-3). This technique will significantly
increase the efficiency of looking for emission line objects ahead of WFMOS spectroscopy, particularly
given the likelihood of improved multicolor data from KIDS, DES, Pan-STARRS or HSC. For
photometry in the BRI bands as in the DEEP2 target selection catalog, a predicted scatter on the log of theequivalent width of 0.21 can be achieved. This should improve with more bands (including U) and should
also improve if these bands are deeper as shown by Abdalla et al. (2008).

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Figure 6-3: Neural network prediction of the [OII] equivalent width given BRI data used to pre-selectDEEP2 galaxies. With more bands it will be possible to improve this performance.

6.5

Spectroscopic Simulations – Dark Energy Surveys
We now turn to a more rigorous estimate of the exposure times necessary to retrieve reliable redshifts

for the low and high redshift DE surveys. This is done by simulating galaxy spectra with different signal
to noise ratios and attempt to retrieve their redshifts.
For the low redshift survey we simulate a galaxy at z = 1.5 and concentrate on the [OII] doublet. We
proceed in two ways: we compute simulated spectra and fit the line directly and also determine a figure of
merit to determine the redshift. Both approaches agree on the signal to noise required.
In order to find a figure of merit to determine the redshift, the Figure 6-4 (Right) shows our definitionof “Significance.” We measure the flux within two windows centered in the 3726 Å and 3729 Å lines,
width ±1 Å (shaded region). For each choice of redshift we compare the flux in these regions as a ratio of
the RMS of the continuum flux in the same spectral region. The product of ratios for the blue and the redlines of the doublet is equivalent to a conditional probability of having both lines detected. From these
low redshift simulations we find a minimum signal to noise of 7 for the [OII] line is necessary for acorrect determination.

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Figure 6-4: (Left) Simulation of WFMOS spectra using realistic sky subtracted noise. We show as an
example a galaxy at the high redshift end of the range for the z~1 DE survey. (Right) Illustration of thesignificance needed in order to confirm that the line is indeed [OII].

For the high redshift survey we simulated three possibilities:(a) The redshift is detected by the line emission (Lyα)(b) The redshift is detected by the Lyman break(c) The redshift is detected by absorption lines seen in a prominent continuum98

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Figure 6-5: Simulations of high redshift Lyman break galaxies in two different cases: via absorption
lines (top) and via Lyman alpha emission (bottom). The absorption line detection requires a continuum
signal to noise of four and the emission line detection requires a continuum signal to noise of one.

Our simulations indicate that redshifts can be secured through either absorption or emission line
properties. A continuum signal to noise ratio of four is required for the former, but a much smaller
integration time would be needed for galaxies with Lyα emission.
According to Shapley et al. (2005), 25% of LBG have a Lyα line strength comparable to thesimulated galaxy with EW = 20 in Figure 6-5. Hence a practical proposition is to proceed with multiple
exposures which would combine to provide a continuum signal to noise of four, reconfiguring on the fly
as emission line sources are detected in individual sub-exposures.

6.6

Target Catalogs – Galactic Archaeology
Provision of suitable source catalogs for the main GA surveys is straightforward due to the relatively

bright photometric depths that define the main surveys.
All-sky coverage in the near-infrared is already provided by 2MASS (Cutri et al. 2003) which is an
excellent source of J,H,K photometry for selecting relatively clean samples of the redder K- and M-giantsout to ≈100 Kpc. More recent large area surveys being conducted on UKIRT using WFCAM

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT(e.g.,Lawrence et al. 2007), and shortly to begin with VIRCAM on VISTA, will provide essentiallycomplete coverage at signal-to-noise of 10:1 to K=18, H=19 and J=20 for all GA regions of interest.
In the optical these surveys are already supplemented by existing SDSS u,g,r,i,z coverage of over
10,000 sq degrees of mainly Northern hemisphere locations with a survey depth equivalent to V = 22 at
signal-to-noise of 10:1 (e.g., Adelman-McCarthy et al. 2008). The SDSS Segue extensions can also be
used for general object selection in lower latitude regions. In the southern hemisphere using the VLTsurvey telescope VST, several public surveys, including a large area Galactic Plane survey and a large
area high latitude survey are due to begin at the end of 2009 and will survey to at least a comparable
depth as SDSS in the North.
The region around M31 and M33 is being targeted in the PAndAS project (PI Alan McConnachie)using MegaCam on the CFHT to image in g- and i-band to AB magnitudes of 26 and 25 respectively. By
the end of the current observing season (2008B), this will have covered half of the entire halo of M31 inprojection out to 150 Kpc, plus an extension around M33. This is a continuation of a successful M31
survey project (e.g.,Ibata et al. 2007) and by end of 2010 will have finished surveying the entire M31 haloout to roughly 150 Kpc in projection, some 300 square degrees of sky.
This effectively opens up access to all planned targets to the full survey depth without the need forcommissioning special photometric surveys. In addition, for the Galactic work, the full optical--NIR
range enables exquisite control over target selection, allowing a focused approach in the more crowded
regions of the Galactic Plane and Bulge.

6.7

Sky Subtraction
Sky subtraction by `nod and shuffle’, as proposed in the Feasibility Study, is hardly an efficient use of

a 8 m class telescope since it requires four times longer integration to achieve the same rms signal/noise(half the time on target, twice as much sky variance). Alternative strategies of nodding to target on adifferent fiber halve this loss but at the expense of dedicating half of the fibers to sky measurements.
Can more sophisticated sky subtraction strategies coupled with a more stable fiber-fed spectrographdesign eliminate the need for nod and shuffle? For a stable fiber-fed spectrograph there are very
promising alternative strategies (e.g., Tolstoy et al. 2004 with FLAMES data; and Wild and Hewitt 2004
on SDSS data). We have investigated these, extending the above model fitting and PCA decomposition
approaches by making use of Genetic Algorithms and Artificial Neural Networks. Recovering up to afactor four in telescope time for a given survey is a sufficient motive!
Here we summarize the results of our simulations, first for stellar spectra and then for galaxy spectra.
FLAMES data, available at comparable LR and HR resolutions provides a good benchmark for our
tests. An example of a LR spectrum in the CaT region produced by the GIRBLDRS FLAMES pipeline isshown in Figure 6-6. The numerous skylines visible in this part of the spectrum not only serve as an
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independent check on the overall wavelength calibration, but also enable an update of the wavelength
calibration of the individual spectra. Modeling the sky spectrum using dedicated sky fibers works wellproviding the requisite processing steps are included (e.g., Batagglia et al. 2007) and we show an example
of such automated sky subtraction and the necessary steps to implement it for processing FLAMES
spectra are described below.
The first step is to combine all the sky spectra (typically, 10–20 sky fibers were allocated) using ksigma clipping to remove spurious features. The average sky spectrum is then split into continuum andsky-line components using an iterative k-sigma clipped non-linear filter (a combination of median andboxcar). The average “sky-line” spectrum is used to define a sky-line template mask, essentially to isolate
those regions of the sky spectrum with significant features and mask out the remainder.
Processing of individual object spectra then proceeds as follows:•

Each spectrum is filtered as before to split the spectra into a line and continuum component, but
this time additionally masking out those regions affected by sky lines to help in defining thecontinuum;•

The object line spectrum, which includes sky lines, is then cross-correlated with the masked line
component of the average sky spectrum, to provide an accurate differential wavelength update
and(re)interpolated to be on the same wavelength system as the average sky spectrum;•

Sky subtraction then involves finding the optimum scale factor and profile matching kernel that
produces a minimum L1 norm (average absolute deviation) when comparing the masked sky-line
and object-line spectra, and applying it to the line-only spectra;•

Finally, the object continuum is added back to the wavelength-updated object-line spectrum, thesky continuum is removed used the sky-line scaling factor and the sky-subtracted spectrum issaved for the next stage in the processing.

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Figure 6-6: Example of automated LR sky subtraction from FLAMES benchmark test data.

The sky subtraction process described above involves three key components all of which are crucial
to achieve good results. Accurate wavelength registration is absolutely vital for good sky subtraction andis (ironically) facilitated by the presence of copious numbers of strong sky lines. These sky lines areunresolved at this resolution and at the signal-to-noise achieved (see later) readily enables sub-km/s
precision in wavelength alignment. This also has the added advantage of ensuring that systematic offsets
due to wavelength calibration for later derived velocities are negligible. As a final step in this process, all
the average sky spectra from each FLAMES observation are cross-correlated with a chosen reference sky
spectrum and used to put all the observations on the same internal system.
The effects of combining the sky spectra to form an average sky and re-interpolating the object
spectrum to this average wavelength system, almost invariably results in a slight mis-match between the
spectral line profiles of the object and average sky spectra. This is circumvented by applying a Hanningsmoothing kernel to each in turn and finding which combination of smoothed and unsmoothed gives thebest results (as determined by the optimum scale factor). More sophisticated adaptive kernel matching(e.g., Alard & Lupton 2000) is probably unwarranted in this case.
The optimum-scaling factor, which in this case is chosen to minimize the L1 norm rather than thecommoner L2 norm to reduce sensitivity to non-Gaussian outliers, is derived using a simple grid searchwith progressively finer step size. As noted previously, a mask is used to isolate the relevant regions of
the sky spectrum to match. By first removing the continuua from both sky and object, more emphasis isplaced on minimizing the impact of sky lines residuals. Of course, implicit in the sky correction is thereasonable assumption that the derived scale factor for both sky lines and sky continuum is the same.
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In a similar fashion for galaxy spectra, a PCA is performed in the observed-frame sky spectrum,
taking advantage of the correlations between the sky subtraction residuals present in each spectrum. A testof the method on SDSS data shows very good removal of the OH emission features (see Figure 6-7).
Some of our Team members are part of the GAMA survey (essentially 2dFGRS repeated 2 magnitudes
fainter); the PCA method has been applied to sky-dominated fiber spectra in this survey, and gives very
clean results.

Figure 6-7: An illustration of sky subtraction on a SDSS galaxy spectrum. The top panel is the observed
spectrum from the SDSS archive, while the bottom panel is the ‘clean’ spectrum after sky subtraction with
the PCA method. The second and third plots from the top show the ‘sky’ pixels and the reconstructed sky
spectrum, respectively (from Wild & Hewett 2005).

6.8

Fiber Allocation Efficiency
An important aspect of any fiber positioner scheme is the efficiency in which the individual positioner

elements can direct or allocate fibers to the targets of interest for spectroscopy. The positioner allocation
efficiency is defined as the fraction of positioner elements that can be successfully placed on a target ofinterest for a given target density and configuration.
The positioner comprises a large number of elements, each of which has a defined patrol region. If thenumber of targets is very large compared to the number of positioner elements, then every element will
have many targets available to choose from and the positioner allocation success rate will be high. If there
are few targets, then most positioner elements will not have a target available, but most targets will be
successfully allocated. In this limit, the degree of overlap between adjacent patrol regions is lessimportant. It is important, however, that there are not significant gaps between patrol regions, so that the
few targets that do exist will not be missed. At intermediate target density, the positioner allocation

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efficiency will be in between these two extremes, and will depend on the degree of overlap of adjacentpatrol regions.
We have performed Monte Carlo simulations of the allocation process with both fields of randomly
distributed targets, and also a clustered set: a sharp redshift selection of 0.7 < z < 1.3 is used, where thesky distribution is generated using a semianalytic calculation in which the luminosity function is adjusted
to match reality. The coupling between apparent magnitude and clustering should thus be as realistic as ispossible with current knowledge. With such simulations, a variety of numbers of positioners, patrol
diameters, and target densities were considered. The following points emerge from our studies:(1) The fraction of unallocated fibers (i.e., waste) is a function of patrol diameter(2) It is difficult to achieve high completeness (> 80%) on a real sample with a single observation.
Multiple observations of the same field increase completeness at the expense of lower efficiency, as more
fibers are unallocated to targets in the later observations. The most efficient strategy will always be toaccept some incompleteness and some fraction of fibers as unallocated using a single observation per
field, unless there is a strong science requirement for full completeness in a given field.
(3) Magnitude dependent reallocation (i.e., bright objects need shorter exposures than faint ones)
seems unlikely to offer a major improvement in observing efficiency given the survey cases described inearlier sections. A possible exception could be surveys in which the expected exposures are very long (sothat overheads associated with reallocation do not dominate) and without steep number counts, so that
there is sufficient diversity in required exposure times for the ensemble of objects.
(4) Clustering of sources will, of course, have an impact on allocation efficiency. If the probability of
having more than one source in one patrol region and zero sources in a neighboring patrol region go up,then the allocation efficiency will go down. We have investigated this effect for the distribution ofgalaxies for our z=1 BAO survey, and have concluded that this is not a large effect.
(5) The allocation of fibers from the focal plane to the spectrographs has a significant effect onobserving efficiency. If the number of available simultaneous spectra is smaller than the number of fiber
positioners (as it is for the high resolution spectrograph described in the WFMOS Feasibility Study), thenthe efficiency can easily become dominated by the spectrograph capability rather than the fiber positioner
properties.
The result of positioner allocation efficiency simulations for surveys in low-resolution spectrographmode is shown in Figure 6-8.

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Figure 6-8: Efficiency and completeness as a function of target density. Blue points represent theallocation efficiency (the estimated fraction of positioner units that can be successfully assigned to atarget) as a function of target density. Error bars are due to the size of sample variance on the scale of
one target field. The black line is the ideal allocation efficiency as might be achieved by a plug-platesystem. In red is the estimated completeness (i.e., the fraction of targets successfully assigned to a
positioner) as a function of target density, assuming one observation per field.

For high spectral resolution, the spectrographs can support up to 600 simultaneous spectra. A flexible
subset of the 2400 positioners is used for target acquisition, while the remaining are steered underneath
Blocking Spots to reduce unwanted sky background. We have simulated the allocation efficiency for thisscheme; the result is shown in Figure 6-9. This approach leads to significantly higher allocation efficiency
than could be achieved by only using a fixed subset of the 2400 positioners.

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Figure 6-9: Positioner allocation efficiency for the high-resolution spectrograph mode versus number oftargets in the field. Shown are the efficiencies for ideal allocation (black), such as one might expect from aplug-plate allocation scheme, and the WFMOS high resolution spectrograph mode (green). The WFMOS
efficiency is superior to that achieved by a system that selects a fixed subset of fibers to route to the highresolution spectrograph (in blue), as assumed in the FS. Shown in red is the fraction of astronomicaltargets successfully assigned to a fiber.

As noted earlier, and is evident in the above Figures, the allocation efficiency depends on the surface
density of available targets. For stellar surveys, Table 6-2 below shows the relevant densities as a functionof magnitude and galactic latitude.
Table 6-2. Estimated V-band stellar density (per square degree)
as a function of galactic latitude and magnitude limit.
|b|=
20°
30°
60°
90°
V = 17
2290
1318
468
355
18
4074
2239
741
550
19
7079
3631
1122
832
20
11482
5623
1698
1230
21
17378
8128
2455
1778
22
23988
11220
3467
2399

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6.9

Fiber Diameter
In order to assess the issue of the optimum fiber diameter, we conducted simulations based on actual

Subaru SuprimeCam imaging data taken in 0.72 arcsec seeing - close to the median value for theobservatory. We examined the fraction of total flux captured by the fiber as a function of fiber diameter,
assuming negligible astrometric error.
In typical conditions, 20% of the PSF flux remains outside the fiber, even for a 2.0 arcsec diameter.
Coupling this analysis with the sky background leads to a signal/noise versus diameter shown in Figure 610, which concludes a diameter of 1.2 arcsec is optimal – somewhat larger than in the FS.

Figure 6-10: Signal to noise ratio (S/N) relative to peak S/N as function of fiber angular diameter, as
measured in 0.72 arc sec seeing (i.e., close to the expected median seeing) with existing Subaru imaging
data. Red points show curve-of-growth of flux for stellar objects. Green data points are S/N vs fiber
diameter for bright stellar objects (i.e., negligible sky background), while black data points are for objects
with similar mag to sky brightness per sq arcsec. Blue points show S/N for objects 3–4 mags fainter than
sky.

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6.10 Estimated Survey Time6.10.1 Dark Energy
For the low-redshift dark energy survey (DELZ), we will rely primarily on emission line galaxies,
using [OII] to identify the redshift. The allocation efficiency of the positioner system depends on thenumber of targets in the field, as shown in Figure 6-8. The desired surface density of galaxies is1245 gal/deg2, as described in Section 3. For a single observation of a field at this density, the averagecompleteness is 67%, with some variation from field to field. We adopt a strategy of re-observing 20% of
the fields, selecting the ones with the worst completeness. In the re-observed fields, a different set oftargets is observed. This boosts the average completeness of the survey, as shown in Figure 6-11. It also
has the benefit of allowing the repeated fields to be used as a systematic check on the results of thesurvey.
To reach our goal of measuring 4 million galaxies, assuming an 80% successful redshift fraction, we
find we need 2380 fields to be measured once and 473 fields to be measured twice. The details appear in
Table 6-3. From similar considerations, we find that we need a catalog from which to select objects totarget that has a surface density on the sky of 2132/deg2 (Table 6-4). Using DEEP2 number counts, wefind the required limiting magnitude is RAB < 23.0. The total survey time is summarized in Table 6-5.
Table 6-3: Number of fields required for low redshift dark energy survey. We adopt a two-tier strategy
where most (80%) of fields are observed once. The 20% of fields with the worst completeness are reobserved, yielding higher average completeness with minimal additional survey time.
Low Redshift Dark Energy (DELZ) Survey - Field Strategy
No. galaxies
Number ofRedshift
Number of
Average
Number of
positioners
Success Ratetimes field iscompletenessfieldsobserved
per field
0.8 x 4 x 106
2400
0.8
1
0.70
2380
0.2 x 4 x 106
2400
0.8
2
0.88
473

Table 6-4: Required density of galaxies for the low redshift dark energy survey. The required surface
density of successful redshifts is 1245/deg2. To achieve this density, we must start with a catalog ofgalaxies that reaches RAB = 23.0.
Low Redshift Dark Energy (DELZ) Survey Galaxy Density
Required Surface Density
Redshift Success Rate
Average survey
Required Catalog Density(/deg2)completeness(/deg2)
1245
0.8
0.73
2132

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SurveyLow Redshift(DELZ)High Redshift(DEHZ)

Table 6-5: Survey time requirements for the dark energy surveys.
Summary of Dark Energy Survey Times
Exposure Time incl.
No. Fields
No.
Total Obs.
overheads (hrs)w/ 1 obs.
Fields w/ 2 obs.
0.25
2380
473
3326
4

69

-

69

Survey Time(hrs/nights)
832 / 83.2
286 / 28.6

Figure 6-11: The red curve shows a histogram of the completeness of our survey fields. We haveassumed that 80% of the fields are observed once (which gives the leftmost bump in the red curve), and
20% of the fields with poor initial completeness are reobserved, yielding higher completeness (rightmost
bump in red curve). Shown in blue is the tail of the distribution that would have remained if these 20%
were not re-observed.

The high redshift survey target allocation efficiency will be similar to the low-redshift survey.
Angular clustering will be less, however, and we therefore dispense with the two-tier observation
strategy, and instead rely on a single observation per field at an average completeness of 67%. The result
is that 69 fields must be observed to reach 105 galaxies for the high-redshift survey, as summarized inTable 6-6.
Table 6-6: Number of required fields for the high redshift dark energy survey.
High Redshift Dark Energy (DEHZ) Survey - Field Strategy
No. galaxies
Number ofRedshift
Number of times
Averagepositioners
Success Rate
field is observed
completenessper field
105
2400
0.9
1
0.67

Number offields69

Section 6.4 describes simulated galaxy spectra. These simulations indicate that a continuum s/n ratioof 4 is sufficient for secure redshift identification using absorption line features. The corresponding

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exposure time of 4 hours results in a total survey time for the high redshift dark energy survey of 286
hours.

6.10.2 Galactic Archaeology
From the estimated WFMOS system throughput, we can estimate the time required for the GA
surveys. Exposure times estimates were validated through comparison to FLAMES. Included in theseestimates is a measure of the fiber allocation efficiency. The allocation efficiency for the Low Resolution
Galactic Archaeology Survey (GALR) follows the same function as the two dark energy survey modes.
For the High Resolution Galactic Archaeology Survey (GAHR) mode, however, the allocation efficiency
differs. The spectrograph is capable of producing up to 600 simultaneous spectra in this mode; thiscapability needs to be efficiently assigned to targets. The result is depicted in Figure 6-9.
The GA survey design is described in section 4, and requires observing 1530 fields for the Low
Resolution Survey, and 1125 fields for the High Resolution Survey. We have also described a potential
future survey (beyond the baseline GA survey) of 230 fields in M31 & M33. The total time required forthese surveys is summarized in Table 6-7.
Table 6-7: Galactic Archaeology survey time requirements.
Galactic Archaeology Survey Time Requirements
SurveyExposure Time
No. Fields
Survey Time(hrs)
(hrs/nights)
Low Resolution
0.5
1530
765 / 76.5
High Resolution
1
1125
1125 / 112.5
M31/M33 (stage 1)
6100
600 / 60
M31/M33 (stage 2)61301300 / 130

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7SCIENCE REQUIREMENTS SUMMARY
Listing specific science objectives and requirements is useful and necessary for deriving instrument

requirements and in understanding the flow of requirements from science to detailed engineering. In thissection, the science requirements discussed in previous sections are summarized in a formal list. The list
does not contain the derived instrument requirements, such as detector wavelength range andspectrograph throughput; the lower level requirements are described in the Functional Performance
Requirements Document. The required wavelength range and spectral resolution are not consolidated into
one master science requirement – listing these as separate requirements for each survey has allowed us todevelop an instrument concept with separate spectrograph modes for the four surveys. The resulting
flexibility has resulted in an optimized instrument design.
Below we list three categories of science requirements: (1) science objectives, (2) top level science
requirements, (3) derived observing requirements. The science objectives describe the science results that
we seek, but, because they may involve some assumptions about the universe are not directly verifiable.
The top level science requirements and observing requirements are intended to be directly verifiable
items. The context and motivation for the science objectives and survey requirements are described inearlier sections. Each item is given a short description. The designator “L1.X” is used for each of theserequirements to indicate that they are Level 1 Science requirements and to distinguish them from theInstrument and Subsystem Requirements at Level 2 and below. The lower level requirements aredescribed in the Initial Functional Performance Requirements Document.

7.1

Dark Energy Science Objectives and Top Level Requirements
The dark energy requirements are:•L1.1: (Science Objective) WFMOS should use the BAO method to measure the distance to amean redshift 1.2 with an accuracy of 0.6%, thereby providing a constraint on w at the pivot
redshift to 3%. The survey will cover a range of redshifts, from 0.7 to 1.6
Rationale: The survey redshift coverage and precision on w is complementary to otherplanned surveys. The context is described in sections 3.6.1 and 3.6.2.

•

L1.2: (Science Objective) WFMOS should use the redshift space distortions technique tomeasure the growth factor, fg(z) ≡ d ln δ / d ln a , in 4 independent redshift bins between redshifts
1 and 3 to a precision of 0.02 near redshift 1 and a precision of 0.1 near redshift 3.

Rationale: The redshift distortion survey provides an important, complementary probe ofdark energy by measuring the growth rate of density fluctuations. The survey redshift
range and precision would provide a major advance over the current state of knowledge
and over currently planned experiments. The context is described in Sections 3.6.2 and3.6.3.
•

L1.3: (Goal) WFMOS should be designed to accomplish objectives L1.1 and L1.2 in less than2000 hours of observing time.

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Rationale: As described in Sections 1 and 3, the Subaru telescope is the primary resource
available to the Japanese scientific community and will be in demand for a variety ofscientific projects. The goal survey time represents our view of an acceptable allocation.
The current survey estimate (Section 6) for the required survey time is 1120 hours.
•

L1.4 (Requirement) WFMOS shall conduct a Low Redshift Dark Energy spectroscopic galaxy
survey with the characteristics described in following table:
Low Redshift Dark Energy Survey
Redshift Range
0.7 ≤z≤ 1.6
Number of galaxies> 4,000,000
Target density for successful redshifts
1245 / deg2
Incorrect Redshift Fraction< 1%
Redshift precision, accuracyΔz < 0.001

Rationale: The above survey parameters are required to provide sufficient accuracy for
the BAO technique, including sufficient cosmological volume, in order to support L1.1,
and the redshift 1 portion of L1.2 The parameters are derived in section 3.6.2. The
approximate target density for successful redshifts is discussed in both section 3.6 andsection 6.

•

L1.5 (Requirement) The Low Redshift Dark Energy Survey shall meet the following observing
properties:
Low Redshift Dark Energy Survey – Derived Observing Requirements
Spectral range
6300-9700 ÅSpectral Resolution
3500
Magnitude Limit
RAB < 23.0
Line S/N ratio>7

Rationale: These properties must be met in order to achieve the survey required by L1.4
and are directly derived from it, as described in section 3.6.2. The required spectral
range allows the measurement of the [OII] 3727 Å spectral double over the required
redshift range. The spectral resolution is required to be sufficient to resolve the doublet,
providing a unique identifier. An absolute minimum of 1340 is required to resolve thedoublet. Higher resolution (e.g., up to 5000) may reduce sky brightness contamination,
but starts to incur a detector read noise penalty, producing less efficient obesrvations.
The current design is a resolution of 3500, which is a balance between these factors. The
magnitude limit is chosen to provide a sufficient source density so that the successful
target density requirement of L1.4 can be met.

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L1.6 (Requirement) WFMOS shall conduct a High Redshift Dark Energy spectroscopic galaxy
survey with the following characteristics:
High Redshift Dark Energy Survey
Redshift Range
2.5≤z≤3.3
Number of galaxies> 100,000
Target density for successful redshifts
1000 / deg2
Incorrect Redshift Fraction< 1%
Redshift precision, accuracyΔz < 0.001

Rationale: The above survey supports L1.2, a redshift distortion measurement, and is
described in section 3.6.3.
•

L1.7 (Requirement) The High Redshift Dark Energy Survey must meet the following observing
requirements.
High Redshift Dark Energy Survey – Derived Observing Requirement
Spectral range
4200-6500 ÅSpectral Resolution
1500
Magnitude Limit
R < 25
Continuum S/N ratio>4

Rationale: These requirements are derived from L1.6 and are described in section 3.6.3.

7.2

Galactic Archaeology Science Objectives and Top Level Requirements
There are five top level science objectives for the Galactic Archaeology Science Case:•

L1.10 (Science Objective): WFMOS should measure the mass, extent, substructure, and otherproperties of the Milky Way dark matter halo through characterizing approximately 30% of
substantial stellar streams in the halo.

•

L1.11 (Science Objective): WFMOS should perform kinematic measurements of dSph
candidates to confirm their status and measure the masses, density profiles, and extent of dSphs.

•

L1.12 (Science Objective): WFMOS should measure the extent, (sub)structure, and kinematics
of Milky Way stellar populations.

•L1.13 (Science Objective): WFMOS should compare the overall history and properties of the
Milky Way with M31 and M33.

•

L1.14 (Science Objective): WFMOS should explore the fossil record of chemical evolution ofstellar populations in the Milky Way.

Rationale: The above goals are described in detail in Section 4.1.4.

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L1.15 (Goal): WFMOS should be designed to accomplish the GA science objectives in less than2500 hours of observing time, with a nominal allocation of 2/3 time to the high resolution survey
and 1/3 time to the low resolution survey.

Rationale: As described in Sections 1 and 4, the Subaru telescope is the primary resource
available to the Japanese scientific community and will be in demand for a variety ofscientific projects. The goal survey time represents our view of an acceptable allocation.
The current estimate (Section 6) for the required survey time is 1890 hours.
•

L1.16 (Requirement): WFMOS shall perform a low resolution galactic archaeology survey withthe following characteristics:
Galactic Archaeology Low Resolution Survey
Area

2000 deg2

Number of stars

3 x 106

Resolution

5000

Sensitivity Limit

V=20, S:N = 10-15 / Å

Wavelength coverage

8150 – 8850 Å
4800 – 5500 Å
0.1 Å
5%
5%

Wavelength centering
Continuum Estimation Accuracy
Relative Flux Calibration Accuracy

Rationale: The survey properties are derived from science objectives L1.10 – L1.13, andare described in detail in section 4.1.
•

L1.17 (Requirement): WFMOS shall perform a high resolution galactic archaeology survey withthe following characteristics:
Galactic Archaeology High Resolution Survey
Area

2000 deg2

Number of stars

1 x 106

Resolution

20,000Sensitivity Limit

V=17, S:N = 150 / ÅContiguous wavelength coverage

4800 – 6800

Wavelength centering
Continuum Estimation Accuracy
Relative Flux Calibration Accuracy

0.1 Å
5%
5%

Rationale: The survey properties are derived from science objectives L1.14, and aredescribed in detail in section 4.1. The continuum estimate required allows sufficientprecision in measuring equivalent widths. Relative flux calibration allows comparison
with external galaxies.

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8INSTRUMENT DESCRIPTION

8.1

System Description
WFMOS is a fiber fed spectrograph designed to be mounted at the prime focus of the Subaru

telescope located on the summit of Mauna Kea, Hawaii. The instrument allows simultaneous low
resolution spectral observations of up to 2400 astronomical targets. In high resolution mode, WFMOS canobserve 600 simultaneous targets.
We repeat the summary description of the instrument here for reference. Greater detail on the
instrument, interactions, and components appear in the WFMOS Study Summary and in the Detailed
Design Document.
WFMOS comprises components mounted in several locations. System elements required to be at theprime focus of the telescope are housed within a Prime Focus Unit (PFU). The three Spectrographs
along with their Detectors and support systems will be located in a spectrograph room adjacent to thetelescope, above the Naysmith focus.
WFMOS will use several Subaru provided elements that are being developed for theHyperSuprimeCam (HSC) instrument. These include the HSC Field Rotator, Hexapod, structure, anda Wide Field Corrector (WFC).
The filter and dewar window used with HSC are replaced by the WFMOS Field Element, locatedclose to the telescope prime focus. The Field Element is a flat window of 30 mm thickness and enables
efficient coupling of the light from the WFC to the WFMOS fibers. It compensates for the removal of the
Dewar window and filter that are part of the design for HSC. The Field Element also contains an array ofsmall obscuring spots under which unused spectrograph fibers can be parked.
An array of 2400 optical fibers is located in the PFU, with each fiber tip position being controllable
in-plane by a piezo-electric Fiber Positioner, nicknamed “Cobra.” Each fiber tip can be positioned within
its 9.5 mm diameter circular patrol region. The patrol regions are in a hexagonal close-packed pattern,with 8 mm separation, and fill a hexagonally shaped 1.4 degree field of view. The overlap between
adjacent patrol regions enables 100% sky coverage of this hexagonal field. The hexagonal shape of the
field of view allows efficient tiling of the sky for large area surveys.
The fibers can be reconfigured for new observation fields in 40 seconds, with each fiber tip placed toan accuracy of 5 microns (corresponding to less than 0.054 arc seconds on the sky). Fibers tips aretranslated (rather than tilted) within the image plane, providing uniform coupling efficiency for all
configurations.
The small fraction of fibers which are not allocated to astronomical targets will measure the spectrumof the sky. In the case of the GAHR survey, which is cross-dispersed, three out of every four fibers is115

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obscured by positioning the fiber tip behind a small dot on the back of the field element to avoid
overlaying spectra or adding unnecessary sky background.
The Fiber System consists of 2400 fibers which relay light incident at the instrument’s focal plane tothree spectrographs located remotely in a room adjacent to the telescope. The fibers are 60 meters inlength and are divided into three groups providing 800 inputs to each spectrograph. A key component of
the fiber system is a Fiber Connector which allows most of the fiber bundle to be permanently installed
to the telescope while facilitating the removal of the PFU as necessary. The connector is located fivemeters from the positioner end, allowing removal of the Top End Unit while leaving 55 m of the fiber
bundle on the telescope structure.
After the science fibers enter the spectrograph room, they are divided three ways and arranged aslinear arrays of 800 fibers at the input to each of three identical Spectrographs. The Spectrographs
provide the capability to simultaneously measure 2400 spectra in low-resolution mode and 600 spectra in
high-resolution mode. During high-resolution spectral observations, the unused fibers are positioned
underneath Blocking Spots to reduce the sky background contribution to the spectra in the highresolution mode. Each Spectrograph uses a Schmidt optics system composed of a collimator, grating, andcamera optics which are reconfigurable to support the four primary science surveys, which requiredifferent spectral resolutions and spectral ranges.
At the image plane of each spectrograph is a Detector System consisting of a pair of close butted
2880 × 5760 pixel CCDs inside a dewar and associated cooling system. The 800 spectra are dispersed
along the columns (5760 pixels), so the gap falls between spectra. In the case of the GAHR survey, the200 cross dispersed spectra fill the detector array. The CCDs have exceptionally high quantum efficiency,
which improves overall instrument efficiency.
The Acquisition and Guide System is composed of four cameras located on the instrument optics
bench in the PFU. Once calibrated to the fiber optic focal plane the Acquisition and Guide System will
provide feedback to update the telescope’s pointing and field rotation by tracking on guide stars within
the cameras’ field of view.
There are four Metrology cameras, mounted on the prime focus support struts looking down at backilluminated fiber tips via the primary mirror of Subaru. The metrology cameras determine the location of
the fiber tips, allowing accurate positioning of the fibers on the selected science targets. Each metrology
camera consists of imaging optics with a CCD detector and includes an internal flat field source.
Calibration of geometrical distortion inherent in the optics of the metrology cameras is achieved byhaving 154 illuminated fixed Fiducial Fibers whose positions are known accurately. Ninety one of the
fixed fiducials are within the bounds of the hexagonal field of view, while 48 fixed fiducial fibers are atthe perimeter of the hexagonal field. Beyond the perimeter of the hexagonal field, fifteen are used to116

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provide encoding of the rotator angle. The fiducial fibers also allow correction for geometric distortion in
the WFC, through measurement and modeling.
The field of view of each Metrology camera is designed to image 1/4 of the optics bench fibers. The
cameras image the backlit science fibers by viewing the optics bench through the telescope optical system
including the WFC and the primary mirror. The centroid of each fiber image is calculated and used todetermine each science fiber position with respect to the fixed fiber locations. This coordinate system isaccurately referenced to the A&G camera allowing accurate placement of astronomical targets on thescience fibers.
The science fibers are back-illuminated by sources located inside the three spectrographs. Only one
set of 800 fibers is illuminated at a time, in a manner allowing no ambiguity in determining fiber positionsin the case that two fibers occupy a region of overlapping patrol regions.
The Prime Focus Unit (PFU) will house the WFMOS Optical Bench which contains the Field
Element, Fiber Positioner System, Acquisition and Guide System, and components of the Fiber System.
Additional components of the Fiber System, Power Distribution System and Thermal System arecontained in the top end of the PFU. The PFU can be installed and removed from the telescope as a single
unit. The optical bench is mounted to the Rotator within the PFU via a bi-conic structure. This structure istuned in stiffness to compensate for differential flexure between the WFC and the Rotator interface where
the WFMOS components are mounted.
The Power Distribution System is located within the PFU to provide power conditioning and control
to the electronics associated with the Fiber Positioner System, the Acquisition and Guide System, and theMetrology System. The distribution system interfaces to the telescope facility power and receives power
control inputs from the System Control function. The Thermal System provides the interface between
the telescope facility cooling system and the instrument systems located in the PFU to prevent excess heatfrom disturbing the telescope’s viewing environment.
The Spectral Calibration Lamps provide the capability to measure the spectral response of the
WFMOS instrument by providing a spectral source at the input to the telescope. The calibration lamps
will be mounted on the telescope apart from the Prime Focus Unit.
The System Control provides the capability to manage an observation’s preparation and execution.
Preparation tasks include parsing large surveys into a series of individual observations, matching fibers todesignated targets and selecting appropriate guide stars, predicting exposure times, and describing
observations in a Subaru-format file. During telescope observations the System Control orchestrates the
instrument systems to achieve successful data collection, sequences the Metrology and Fiber Positioner
Systems to achieve target acquisition with the fibers, interfaces to the Subaru Observatory Software
System, and provides operator interfaces for quick-look data and instrument health and safety displays.

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENTThe System Control also provides the capability to perform high level engineering tests to aid in
instrument integration and troubleshooting.
The Data Reduction function receives raw data read from the Detector System and uses it to produce
calibrated spectra ready for scientific analysis. The Data Reduction function provides the capability to
reduce data of varying spectral resolution and saves all spectroscopic and image files in the required data
format. The Data Reduction function will also provide the capability to quickly assess the quality(completeness, signal-to noise ratio, etc.) of the data collected.
A typical observation proceeds as follows. After initialization, instructions are executed to configure
the telescope and instrument for the desired observation. The details of each observation are pre-planned
using software provided by the System Control to aid the researcher in orchestrating the survey. Using
information derived from a catalogue of astronomical objects, the field configuration including thepositions for each of the 2400 fibers is computed prior to the start of the observation. The spectrographconfiguration is determined by which of the 4 primary science surveys is being executed. A typicalobserving night might use more than one spectrograph configuration in order to optimize survey
efficiency as sky brightness or the seeing changes.
Execution of an observation begins with moving the telescope to the desired field center. While thetelescope is slewing, the Fiber Positioning System executes commands from the Control System tosimultaneously move each of the 2400 fibers to their required positions. The Metrology System is used toverify each fiber’s position. The positioning process proceeds as follows:
1. The current position of each science fiber is determined relative to a set of fixed fiducial fibers.
These fixed fibers are back-lit by a dedicated calibration lamp. The back-lit fibers are imaged by
the Metrology System and the centroid of each fiber is calculated. Similarly, the 2400 science
fibers are back-lit by calibration lamps in each spectrograph and imaged by the Metrology
System. The centroids of each science fiber image are computed in order to determine theirrelative positions.
2. The Control System computes and issues the necessary commands to the Fiber Positioning
System to move the 2400 Science Fibers to the positions required for the observation. The
metrology and the fiber movements are coordinated in 3 groups of 800 fibers. Some operations
proceed in parallel.
3. Steps 1 and 2 will be repeated several times to accurately position each fiber.
The positioning process is estimated to take approximately 40 s from start of telescope pointing tocompletion of fiber positioning. In parallel with this process, the Acquisition and Guide System refines
the pointing of the telescope. The Acquisition and Guide System provides feedback to the telescope
pointing and rotator position during the observation. The hexapod is commanded to the appropriate
position to maintain the WFC alignment in the presence of flexure in the PFU.

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Once positioning is complete, the Detector System collects data. The Data Reduction Systemproduces quick-look information on the completed observation so to that survey progress can bemonitored.

8.2

Instrument Operational Modes
The WFMOS instrument will support the following types of operational mode at the telescope:•

Start-up•Target Acquisition•

Observation•

Calibration•

Testing

A typical observing night will consist of the use of several of these modes. We describe each of thesemodes below. The descriptions are necessarily preliminary; greater detail will be developed in the courseof the progression of the instrument design and construction.
In addition, the health and status of the instrument will be monitored continuously, and is therefore
not listed as a separate operational “mode.” The “monitor status” function is described in Section 9.

8.2.1 Start-up
The Start-up mode is used to initialize the WFMOS instrument at the beginning of an observing
session. All subsystems are powered up, self-tested and made ready for observation. The observations to
be made during the session are selected.
•

Power systems on.

•

Initialize subsystems.

•

Perform power-up tests.

•

Load the description of the observations to be made.

Start-up also includes a subset of commands used to configure the spectrograph (selected grating,filter wheel setting, camera angle, etc). These commands will generally be used once at the beginning of
the observing night. The system allows the spectrograph to be reconfigured for a different spectral mode
during the course of the observing night, as might be useful to optimize survey strategies with respect toweather, moonrise, or other constraints.

8.2.2 Target Acquisition
The Target Acquisition mode is used to acquire a new target field for observation. If the new target
field is at a different location on the sky from the previous observation, the Subaru telescope is slewed toa new target field. At the same time the positioner subsystem is configured to place the science fibers at
the expected positions of the science targets in the focal plane (the positions having been calculated inadvance by the observation preparation system). The metrology camera is used to provide feedback on the

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position of the fibers. When the telescope has reached the target field it begins tracking. The instrument
rotator also begins tracking the field. The autoguider CCDs locate the guide stars (whose positions are
known relative to the science targets) and start sending tracking corrections to the Subaru TCS. The
system aligns the target field.
•

Acquire target field with acquisition system.

•

Enable ADC operation.

•

Focus and identify stars on at least two of the autoguider detectors to determine telescope offset
and rotational offset.

•

Command telescope to initiate offset to bring stars to required position•

Lock in acquisition using the Acquisition and Guide System (A&G).

•

Fine tune focus using the A&G system.

•

Initiate guiding.

8.2.3 Observation
In Observation mode, the WFMOS instrument carries out the steps needed to make scienceobservations. For efficiency, this mode may be overlapped with Target Acquisition (e.g., the instrument
may be configured at the same time the telescope is slewing, and the telescope may be slewed to the next
target as soon as the instrument and detector systems have finished acquiring data). The spectrographs are
configured as required for the observations and the detectors are set to make one or more science
exposures, as requested by the user. At the conclusion of each exposure, the data are read out and passed
to the Data Pipeline for archival and online data reduction.
•

Take required number of exposures.

•

Fine tune fiber positions as required ( For observations with a total time longer thanapproximately 60 minutes, fibers will need to be repositioned to account for the changes in theastrometric distortion due to atmospheric refraction. A detailed plan for this will be developed
before the instrument Preliminary Design Review).

•

Reassign fibers to new targets as required in-between exposures to optimize data collection, as
determined by the observation planning software (using Target Acquisition mode). We do notenvision reassigning subsets of fibers between telescope pointings for the four primary sciencesurveys. The instrument system, however, does have this capability inherently, which may be
useful for future additional surveys.

•

Collect FITS header information and send data to online data reduction pipeline and Subaru dataarchive.

•

Execute quick look data reduction at end of each exposure.

The following distinct types of science observation will be made:•Low redshift dark energy survey (DELZ):

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will be less than 100%; unallocated fibers will be used for sky background subtraction.
– The spectrographs are configured into DELZ mode.
– The science data will consist of three images (one per spectrograph), each containing 800
spectra, for up to 800 distinct science targets.
•

High redshift dark energy survey (DEHZ):– All 2400 fibers are available to be allocated to science targets.
– The spectrographs are configured into DEHZ mode.
– The science data will consist of three images (one per spectrograph), each containing 800
spectra, for 800 distinct science targets.

•

Low resolution galactic archaeology survey (GALR):– All 2400 fibers are available to be allocated to science targets.
– The spectrographs are configured into GALR mode.
– The science data will consist of three images (one per spectrograph), each containing 800
spectra, for 800 distinct science targets.

•

High resolution galactic archaeology survey (GAHR):– A specific subset of up to 600 fibers are allocated to science targets.
– The spectrographs are configured into GAHR mode.
– The science data will consist of three images (one per spectrograph), each containing up to
cross-dispersed 200 spectra, corresponding to 200 distinct science targets. Each crossdispersed spectrum will be recorded on the detector in 4 separate segments•

Miscellaneous observation LR and HR:– Miscellaneous observations can be made by mixing and matching features from the above four
survey modes. In addition, the wavelength range and spectral resolution of the spectrographs
can be tailored for a particular science project, and are not limited to the fixed ranges specified
for the survey modes. It may be possible to operate each of the spectrographs simultaneously
in a different mode.
– Used for added value science and miscellaneous observations.
– All 2400 science fibers may be allocated to science targets for low spectral resolution (LR)observations, or up to 600 science fibers may be allocated to science targets for high spectral
resolution (HR) observations.
– The spectrograph is configured to the desired spectral resolution and wavelength range by
selecting the appropriate grating, filter and camera-collimator angle combination.
– The science data will consist of three images (one per spectrograph), whose layout depends onthe chosen spectral resolution.

The low spectral resolution modes (DELZ, DEHZ, GALR and Miscellaneous LR) will require adifferent data reduction procedure from the high spectral resolution modes (GAHR and MiscellaneousHR), which generate cross-dispersed spectra.

8.2.4 Calibration
The Calibration mode is used to calibrate the instrument so that the observed data can be converted
into a form that has most instrumental signatures calibrated or removed. A variety of calibration frames
are required including, but not necessarily limited to, the following: bias, dark, flat field, and wavelength.

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It is a goal to have the spectrographs stable enough during the night and after fiber reconfiguration thatdaytime calibrations will be sufficient for the nightly observations. This will certainly be easy to achieve
for the bias and darks, but may be risky for the flat fields and wavelength calibrations depending on thelevel of calibration required for the data. Further study will be required during the design phase.
•

Obtain required set of biases in the daytime.

•

Obtain required set of darks in the daytime.

•

Obtain required set of flat fields for each fiber configuration using set of calibration lamps
mounted on the spider vane. Goal is to be able to do this during the daytime, but may need to bedone at the time of observation if the fiber performance is not stable against fiber reconfiguration.

•

Obtain required set of wavelength calibration frames using set of calibration lamps mounted on
the spider vane. Goal is to be able to do this during the daytime, but may need to be done at thetime of observation if the spectrograph stability proves to be insufficient or if fiber configurations
impact on the calibration stability.

8.2.5 TestingThis is a mode of operation in which all capabilities of the instrument can be tested for engineering
purposes. This mode of operation can be performed when the instrument is either on or off of thetelescope and is primarily used for maintenance, problem fixing, and diagnostics. Special fixtures may berequired for off telescope testing, such as back illumination of the fibers on the positioner or calibration
lamp illumination of the spectrographs. Many of the instrument components will have a mode whichallows that component to be exercised, and faults reported. Examples include the fiber health system, thepositioner configuration, and the detector readout. The full scope and capability of this mode will be
developed before the Preliminary Design Review.

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9

OPERATIONAL CONCEPT MODEL

9.1

Introduction
To provide insight into how the WFMOS instrument will be used to accomplished the desired sciencesurveys, we provide the following description of system level interactions. The operational WFMOS
system can be divided into four distinct packages:•

WFMOS Observation Preparation—this package includes all the facilities to prepare WFMOS
observations for submission to the observatory. It allows proposals to be submitted, approved andthen prepared for execution.

•WFMOS Observation Execution—this package includes all the facilities for making WFMOS
observations at the observatory.

•WFMOS Pipeline—this package includes all the facilities for processing WFMOS data. It
provides a quick look display to the observer and provides reduced data for the Gemini Science
Archive.

•

WFMOS Engineering—this package contains all the engineering operations that are needed tosupport WFMOS.

The operation of the system can be described by these set of “Use Cases.” The Use Case notation isdefined in Figure 9-1. Detail on each of these is provided in the following sections.

Actor --- someone or something in the outside world
Do something

Do something

Do something

Use Case --- an example of a use of a system<<include>>

Do something else as well

A use case is an
optional extra fromanother

Perhaps do this<<extend>>

A use case is
automatically
executed fromanother

Several actors can be part of a collaboration

One or more use cases can be implemented by a package

A
A
A

B
B
B

Communication between A and B
Dependency: “A depends on B”
Generalisation: “A is a kind of B”

Payment

Credit card

Astronomer

Cash

Survey Scientist

Figure 9-1: Use Case Diagrams Key

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9.2

WFMOS Observation Preparation
Figure 9-2 shows a use case diagram for the WFMOS observation preparation package.

Figure 9-2: Use Cases for WFMOS Observation Preparation

The package needs to support the following operations:
Submit Proposal: An astronomer submits a proposal to make one or more observations withWFMOS, perhaps using the existing Gemini phase 1 submission tool. A proposal can be an idea for an
entire survey, a proposal to make a specific observation, or a list of serendipitous targets that could beobserved during a survey observation if there are any spare fibers.
Authorize Proposal: WFMOS observation proposals are authorized by a Telescope Allocation

Committee and then move on to phase 2. The authorization procedure is decided by the Gemini andSubaru observatories.
Check existing observations: The status of existing observations in the science archive is checked

and reported. There needs to be a facility for checking the current status of each survey, and for listing thefields already observed.

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Define science program: This represents the preparation of a series of WFMOS observations

designed to fulfil an astronomical project. The astronomer specifies the observation in terms of area ofsky to be covered, type of objects to be observed and required magnitude range, and perhaps a list of
wavelength ranges and grating resolutions required for each object. Suitable guide stars are chosen foreach target field. The end product will be a series of individual observations that make up the survey,
which are saved to the “Prepared Observations” part of the Observation Database. Observations made in
adjacent fields can be mosaiced together, or observations in a crowded field may be divided into different
fiber configurations at the same telescope pointing. The exact choice of survey field centers may be
determined by the availability of suitable guide stars (e.g., at the Galactic poles).
We distinguish between five different types of survey preparations namely:•

Define Low Redshift Dark Energy survey•

Define High Redshift Dark Energy survey•

Define high resolution Galactic Archaeology survey•

Define low resolution Galactic Archaeology survey•

Define other surveys (TBD)

Modify science program: This facility allows a prepared science program to be modified. The

observations may be adjusted to cope with a change to the instrument (such as broken fibers) or some newserendipitous observations can be piggy-backed onto an existing science program to make use of the nonallocated fibers.
It is assumed that the Subaru telescope allocation committee will decide which nights will beallocated to WFMOS observations.

9.3

WFMOS Observation Execution
Figure 9-4 shows a use case diagram for the WFMOS observation execution package.

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Figure 9-3: Use Cases for WFMOS Observation Execution

An observing session with WFMOS will need the following operations:Start-up/Prepare for session—At the beginning of the night (or any daytime calibration session)

there are procedures for running up the software, reading the instrument description and testing that all
the equipment is functional. These procedures include a package which can query the observation
database and generates a plan of scheduled observations for the night. This plan needs to take account of
the requirements for each observation and the priority assigned to each observation (which might itself
depend on the completion status of the survey it belongs to).
Execute scheduled observations—The scheduled observations are described in a (OPE) file, which

can be read by the Subaru OCS software. Each of these observations can be executed in sequence or in
any order selected by the operator.
Execute single observation—All the procedures that take place to make a single observation,

including acquiring the target, acquiring guide stars, configuring the instrument, making exposures,
collecting header information and saving the data.

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Execute science observation and Execute calibration observation represent the kinds ofobservation that can be made, with the main difference being that Execute calibration observation could
be executed during the day.
Acquire target includes all the procedures needed to set up the Subaru telescope, including theacquisition of guide stars and the configuration of the positioner.
Shutdown/End of session—All the procedures that need to take place at the end of any session,

including parking the telescope, protecting the instrument and shutting everything down.
Monitor status—The health and status of the instrument will be monitored continuously. The

operator will be provided with display screens to follow the progress of an observation and show
instrument parameters, such as temperature and pressures. Any faults or alarms are reported to theoperator. Any significant events that happen during the night are written to the Observation Log.

9.4

WFMOS Engineering
The WFMOS engineering use cases are summarized in Figure 9-5.

Figure 9-4: Use Cases for WFMOS Engineering

These facilities support engineering work and any other operations necessary to keep WFMOS
operating to full performance:
Install WFMOS on Subaru: Each time the instrument is installed on the Subaru top-end,engineering facilities will be needed to check the instrument has been installed correctly, check the healthof the instrument (e.g., fiber connectivity), update any calibration information that might have changed,
and update the instrument configuration.

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Remove WFMOS from Subaru: When the instrument is removed from the Subaru top-end,engineering facilities will be needed to update the instrument configuration.
Set up and calibrate WFMOS: WFMOS will need maintenance procedures to•

Make one-off calibration measurements, such as measuring the flexure and transmission of thefibers and mapping any bad pixels on the detectors.

•

Make daytime calibration observations.

•

Test and maintain a record of the health and performance of the instrument (see below).

•

Support instrument updates (see below).

Update WFMOS: WFMOS will need engineering facilities to support any changes to the instrument

system. A change could be a new release of the software, a recalibration of the instrument’s configuration
data (following the installation of a new piece of exchangeable hardware, such as a filter), or a quick
repair made during the night to correct a problem.
Test WFMOS: WFMOS will need verify facilities to measure and record the performance of the

instrument. Two such procedures (“POSITIONER metrology observation” and “FeRaS fiber healthmeasurement”) are already known about.

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Madgwick D.S. et al. (the 2dFGRS Team), 2003, MNRAS 344, 847
Majewski S. R., et al., 2005, AJ, 130, 2677
Mateo et al., 1998, AJ, 116, 2315
Matsubara T., 2004, ApJ, 615, 573
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Mcdonald P., Seljak U., 2008, arXiv: 0810.0323
Meiksin A., White M., Peacock J.A., 1999, MNRAS, 304, 851
Meneux B. et al., 2006, A&A, 452, 387
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Moore B., et al., 1999, ApJ, 524, L19
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Muñoz R. R., et al., 2005, ApJ, 631, L137
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Peacock J.A. et al., 2006, ESA-ESO report, arxiv:astro-ph/0610906
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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT
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11

ACRONYMS AND ABBREVIATIONS

1D
2dF
2dFDR
2dFGRS
3D
A&G
A/R
AAO
AAT
ACSIS
ACTDP
AD
ADC
ADU
AES
AF2
AG
AGC
AIV
ALMA
AMNH
ANA
AO
APD
APG
API
APS
AR
ARC
ARGOS
ARL
ARR
ATC
ATP
AURA
BAO
BBAR
bHROS
BIT
BOM
BOSS
BRI
CAD
Caltech
CAS
CCB
CCD
CCW
CDD

One-Dimensional
The 2 Degree Field instrument
2dF Data Reduction
The 2dF Galaxy Redshift Survey
Three-Dimensional
Acquisition and Guiding
Antireflection
Anglo-Australian Observatory
Anglo-Australian Telescope
Auto-Correlation Spectrometer and Imaging System
Advanced Cryocooler Technology Development Programs
Applicable Document
Atmospheric Dispersion Compensator
Analog-to-Digital Unit
Auger Electron Spectroscopy
Auto-fib2
Autoguider
Acquisition and Guidance Camera
Assembly, Integration, and Verification
Atacama Large Millimeter Array
American Museum of Natural History
Analysis workstation
Adaptive Optics
Astronomy and Physics Directorate (JPL)
Annealed Pyrolytic Graphite
Application Programming Interface
Alignment and Phasing System
Antireflection
Astronomical Research Cameras
Abundance and Radial Velocity Galactic Origins and Structure Survey
Applied Research Laboratory
Acceptance Readiness Review
Astronomy Technology Centre
Acceptance Test Plan
Association of Universities for Research in Astronomy
Baryonic Acoustic Oscillations
Broadband AR
Bench-mounted High Resolution Optical Spectrograph (Gemini)
Built in Test
Bill of Material
Baryon Oscillation Spectroscopic Survey
Standard photometric bands (Johnson)
Computer-Aided Design
California Institute of Technology
Central Alarm System
Change Control Board
Charge-Coupled Device
Counter-Clockwise
Concept Design Documentation

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CDR
CELT
CFHT
CGS4
CIB
CIRPASS
CIT
CL
CLD
CM
CMB
CMII
CMM
CMOS
CMP
CMS
CNC
COB
CoDR
COO
CosmoMC
COTS
CPU
CR
CRB
CSRP
CTE
CTIO
CTM
CV
CVD
CW
DAQtk
DAZLE
DBSP
DC
DCS
DE
DEC
DEHZ
DE-HZ
DEIMOS
DELZ
DETF
DHS
DID
DLD
DMA
DoD
DOE
DOF

Critical Design Review
California Extremely Large Telescope
Canada-France-Hawaii Telescope
Cooled Grating Spectrometer 4
Configuration Implementation Board
Cambridge Infrared Panoramic Survey Spectrograph
California Institute of Technology
Center Line; also confidence level
Center for Life Detection
Configuration Management
Cosmic Microwave Background
Configuration Management II
Coordinate Measuring Machine
Complementary Metal Oxide Semiconductor
Configuration Management Plan
Configuration Management System
Computer Numerical Control
Chip on Board
Concept Design Review
Caltech Optical Observatories
A Markov-Chain Monte-Carlo software package
Commercial-Off-the-Shelf
Central Processing Unit
Change Request; also Cosmic Ray
Configuration Review Board
Center for Space Research Programs
Charge Transfer Energy
Cerro Tololo Inter-American Observatory
Contract Technical Manager
Curriculum Vitae
Chemical Vapor Deposition
Clockwise
Data Acquisition Toolkit
Dark Age Z Lyman-alpha Explorer
Double Spectrograph at Hale 200" Cass focus
Direct Current
Detector Control Software
Dark Energy
Declination
Dark Energy, High z / High Redshift Dark Energy
Dark Energy, High z / High Redshift Dark Energy
Deep Imaging Multi-Object Spectrograph (Keck)
Dark Energy, Low z / Low Redshift Dark Energy
Dark Energy Task Force
Data Handling System
Data Item Description
Digital Logic Decoder
Direct Memory Access
Department of Defense
Department of Energy
Degrees of Freedom

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DOORS
DPM
DR
DRR
DS
DSP
E2V
ECN
ECoS
EGSE
ELT
EM
EMC
EMI
ESA
ESD
ESO
EU
FAR
FEA
FIS
FITS
FLAMES
FLCG
FMEA
FMOS
FOCCOS
FOV
FPC
FPGA
FPRD
FRD
FRS
FS
FSL
FTE
Fundep
FWHM
GA
GAHR
GA-HR
Gaia
GALEX
GALR
GA-LR
GCal
Gen2
G-ICD
GMOS
GOODS

Dynamic Object Oriented Requirements System
Deputy Project Manager
Data Reduction
Design Release Review
Data Set
Digital Signal Processor
e2v Technologies PLC, Essex, England
Enterprise Change Notice; also Engineering Change Notice
Eberly College of Science
Electrical Ground Support Equipment
Extremely Large TelescopeEngineering Model
Electromagnetic Compatibility
Electromagnetic Interference
European Space Agency
Electro-Static Discharge
European Southern Observatory
European Union
Final Acceptance Review
Finite Element Analysis
Fiducial Illumination System
Flexible Image Transport System
Fiber Large Array Multi Element Spectrograph
Field Lens Collimator Group
Failure Modes Effects and Analysis
Faint Multi-Object Spectrograph
Fiber Optical Cable and Connector System
Field of View
Fiber Positioner Controller
Field-Programmable Gate Array
Functional Performance Requirements Document
Focal Ratio Degradation
Fiber Routing System
Feasibility Study; also Frame Set
Frequency Standards Laboratory
Full-Time Equivalent
Fundação de Desenvolvimento da Pesquisa
Full Width at Half Maximum
Galactic Archaeology
Galactic Archaeology–High Resolution / High Resolution Galactic Archaeology
Galactic Archaeology–High Resolution / High Resolution Galactic Archaeology
An ESA-led astrometry space mission
Galaxy Evolution Explorer
Galactic Archaeology–Low Resolution / Low Resolution Galactic Archaeology
Galactic Archaeology–Low Resolution / Low Resolution Galactic Archaeology
Gemini Calibration Unit
.Generation 2.
(next generation Subaru Operations System Software (SOSS))
Gemini ICD
Gemini Multi-Object Spectrograph
Great Observatory Origins Deep Survey

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT
GPI
GPMC
GPol
gri
GRS
GSA
GUI
H/W
HALT
HASS
HETDEXHOMERHRHRNIRS
HSC
HTTP
I&T
ICD
ICM
ICS
IFS
IFU
IoA
IP
IPAC
IR
IRAF
IRMOS
IRR
IS
ISOCAM
ISW
IT
ITL
ITO
IVOA
JAC OT
JCMT
JFET
JHU
JPL
JWST
K.A.O.S.
KIDS
KPP
KVM-o-IP
LARS
LBL
LBNL
LDB
LED

Gemini Planet Imager
Governing Program Management Council
Gemini PolarimeterStandard photometric bands (SDSS)
Galaxy Redshift Survey
Gemini Science Archive
Graphical User Interface
HardwareHighly Accelerated Life Test
Highly Accelerated Stress Streening
Hobby Eberly Telescope Dark Energy ExperimentHigh Altitude Ozone Monitoring Experimental Rocket
High Resolution
High Resolution Near Infrared Spectrograph
HyperSuprimeCam
Hypertext Transfer Protocol
Integration and Test
Interface Control Document
Institute of Configuration Management
Instrument Control Software
Integral Field SpectrographIntegral Field Unit
Institute of Astronomy (Cambridge)
Internet Protocol
Infrared Processing and Analysis Center
Infrareda library of astronomical data reduction software
Infrared Multi-Object SpectrographIntegration Readiness Review
Investigation Scientist; also Information System
Infrared Space Observatory Camera
Integrated Sachs-Wolfe
Information Technology
Imaging Technology Laboratory, University of Arizona
Indium Tin Oxide
International Virtual Observatory Alliance
Joint Astronomy Center Observing Tool
James Clerk Maxwell Telescope
Junction Field Effect Transistor
Johns Hopkins University
Jet Propulsion Laboratory
James Webb Space Telescope
Kilo-Aperture Optical SpectrographKilo-Degree Survey
Key Performance Parameter
Keyboard, Video, Mouse over Internet Protocol
Laboratory for Reliable Software
Lawrence Berkeley Laboratory
Lawrence Berkeley National Laboratories
Logic Decoding Board
Light-Emitting Diode136

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LFC
LGS
LISA
LNA
LOE
LR
LRIS
LRU
M1CS
M3
MATLAB
MAXR
MBE
MCT
MDD
MDL
ME
Michelle
MIP
MIRI
MLS
MMR
MMT
MNRAS
MOE
MOP
MOS
MOSFET
MPO
MPS
MRO
MROI
MRR
MS
MT
MTBF
MTHR
MTO
MUSE
N&S
NA
NAOJ
NAS
NASA
NDEAA
NIR
NOAO
NSCAT
NSF
NST
OBCP

The Palomar Large-Format Camera
Laser Guide Star
Laser Interferometer Space Antenna
Laboratório Nacional de Astrofisica
Level of Effort
Low Resolution
Low Resolution Imaging SpectrometerLine Replaceable Unit
M1 Control System
Moon Mineralogy Mapper
Programming language for technical computing from The MathWorks
The maximum resolution spectrograph mode
Molecular Beam Epitaxy
Ministry of Science and Technology
Mechanical Design Description
Micro Devices Laboratory
Mechanical Engineer
Mid-Infrared Echelle SpectrographMaterial Inventory Plan
Mid-Infrared Instrument
Microwave Limb SounderMonthly Management Review
Multiple Mirror TelescopeMonthly Notices of the Royal Astronomical Society
Measure of EffectivenessMeasure of Performance
Multi-Object Spectrograph
Metal Oxide Semiconductor Field Effect Transistor
Multi-Fiber Push-on (a type of fiber optic connector)
Movement Planning Software
Mars Reconnaissance Orbiter
Magdalena Ridge Optical Interferometer
Make Release Review; also Manufacture Readiness Review
Mechanical Specification
Media-termination (a type of connector)
Mean Time Between Failures
Moderate to High Resolution Spectrograph
Motor Toggle OrderMulti-Object Spectroscopic Explorer (ESO VLT)
Nod and Shuffle
Not Applicable; also Numerical Aperture
National Astronomical Observatory of Japan
Network Attached Storage
National Aeronautics and Space Administration
Nondestructive Evaluation and Advanced Actuators
Near-Infrared
National Optical Astronomy Observatory
NASA Scatterometer
National Science Foundation
New Scale Technologies
Observation Control Processor

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OBS
OCD
OCDD
OCS
OD
OFHC
OFS
OG
OIO
OLDP
OPE
ORB
ORR
OSL
OSM
OWS
OzPoz
P2PP
PALM-LGSPanSTARRS
PASP
PBS
PC
PCA
PCB
PCD
PCI
PCO
PDMS
PDR
PDS
PECVD
PFC
PFI
PFU
PHSC
PI
PM
PMS
POP
POSM
PPARC
PRA
PRS
PS
PSA
PSE
PSF
PSR
PSU

Operational Business Schedule
Operational Concept Design
Operational Concept Design Document; also Operational Concepts Definition
Document
Observatory Control System; also Observation Control Software
Optical Designer; also Outer Diameter
Oxygen Free High Conductivity
Fiber connector manufacturer
Output Gate
Oliveira Instrumentos Opticos
On-Line Data Processing
Observation Procedure Editor
Orbital Sciences
Operations Readiness Review
Optical Science Laboratory
Object Selection Mechanism
Observation Workstation
A fiber positioner unit for the Very Large Telescope (VLT)
Phase 2 Proposal Preparation
Palomar Laser Guide Star
Panaromic Survey Telescope and Rapid Response System
Publications of the Astronomical Society of the Pacific
Product Breakdown Structure
Personal Computer
Principal Component Analysis
Printed Circuit Board
Product Concept Design
A computer bus standard
Project Close Out
Project Database Management System
Preliminary Design Review
Power Distribution System
Plasma Enhanced CVD
Prime Focus Corrector
Prime Focus Instrument
Prime Focus Unit
PFU from the HyperSuprimeCam
Project Initiation; also Principal Investigator
Project Manager
Project Master Schedule
Program Operating Plan
Prismatic Object Selection Mechanism
Particle Physics and Astronomy Research Council
Project Resource Analyst
Pipeline Reduction System
Project Scientist
Project Schedule Analyst
Project System Engineer
Point Spread Function
Pre-Ship Review
Pennsylvania State University

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT
PTFE
PTI
QA
QC
QE
QH
QTH
RA
RAVE
RD
Req
RFP
RM
RMS
ROE
ROI
ROM
ROMPS
RR
RSD
RSS
RTD
S/N
S/W
SAA
SAMP
SAN
SARR
SC
SCP
SCUBA
SCUBA2
SDSS
SE
SEGUE
SHARAD
SIA
S-ICD
SIM
SimCam
SIR-C
SIRR
SKA
SKADS
SNAP
SNR
SOAR
SOSS
SOW
SPEC
SPIE

Polytetrafluoroetheylene – Teflon
Palomar Testbed Interferometer
Quality Assurance
Quality Control
Quantum Efficiency
Quartz Halogen; also Quiet Hour
Quartz Tungsten Halogen
Right Ascension
Radial Velocity Experiment
Reset Drain; also Reference Document
Requirement
Request for Proposal
Risk Management
Root Mean SquareRoyal Observatory Edinburgh
Region of Interest
Rough Order of Magnitude
Robotically Manipulated Plug-Plate System
Requirements Review
Redshift Space Distortions
Root Sum Squared
Resistance Temperature Detector; also Real Time Display
Signal to Noise
SoftwareSpace Act Agreement
Simple Application Messaging Protocol
Storage Area Network
System Architecture and Requirements Review
Subprime Cam
Service Connection Point
Submillimeter Common-User Bolometer Array
Submillimeter Common-User Bolometer Array 2
Sloan Digital Sky Survey
Systems Engineering
Sloan Extension for Galactic Understanding and Exploration
Shallow Radar
Simple Image AccessSubaru ICD
Space Interferometry Mission(a working name for the next generation instrument interface)
Spaceborne Imaging Radar–C
System Integration Readiness Review
Square Kilometer Array
Square Kilometre Array Design Studies
SuperNova Acceleration Probe
Signal-to-Noise Ratio
Southern Astrophysical Research Telescope
Subaru Operations System Software
Statement of WorkSpecification
Society of Photo-Optical Instrumentation Engineers

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MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENT
SPIRE
SPLAT
SQR
SRB
SRE
SRF
SRL
SRR
SRS
SSA
ST7
STARS
STCS
STELESSTFC
SUBARU
SW
SWIFT
TBA
TBC
TBD
TBR
TCP
TCS
TES
TFCALC
ThAr
TIKI
TIM
TMC
TMT
TPF
TPM
TUE
UARC
UCL
UCLES
UK
UK ATC
UKIRT
USA
USM
UUV
UV
UVES
V
V&V
VCP
VDD
VIPERS
VISTA

Spectral and Photometric Imaging Receiver
Spectral Analysis Tool
Rotary Squiggle Motors
Strain Relief Box
Spectral Resolution Element
Sequence Request File
Significant Risk List
System Requirements Review
Software Requirements Specification
SuperCosmos Science Archive
Space Technology 7
Subaru Telescope Archive System
Subaru TCS
SOAR Telescope Echelle Spectrograph
Science and Technology Facilities Council
Subaru Telescope Archive System
SoftwareShort Wavelength Integral Field Spectrograph
To Be Announced
To Be Confirmed
To Be Defined; also To Be Decided
To Be Reviewed
Transmission Control Protocol
Telescope Control System
Top-End Structure
Software package used for designing thin films
Thorium Argon
Telescope Interface Kit
Technical Interchange MeetingTechnical and Management Committee
Thirty-Meter Telescope
Terrestrial Planet Finder
Technical Performance Measure
Top-End Unit Exchange
University Affiliated Research Center
University College London
University College London Echelle SpectrographUnited Kingdom
United Kingdom Astronomy Technology Centre
United Kingdom Infrared Telescope
United States of America
Ultrasonic Motor
Unmanned Undersea Vehicle
Ultraviolet
UV-Visual Echelle Spectrograph
Volts
Verification and Validation
Verification and Commissioning Plan
Version Description Document
VIMOS Public Extragalactic Redshift Survey
Visible and Infrared Survey Telescope for Astronomy

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WIDE FIELD FIBER-FED OPTICAL
MULTI-OBJECT SPECTROMETER (WFMOS)OPERATIONAL CONCEPT DESIGN DOCUMENTVISTAIRCAM
VLT
VME
VNC
VO
VPH
VPHG
WBS
WCS
WCSE
WFC
WFCAM
WFMOS
WFS
WHT
WM
WMAP
WWW
WY
WYFFOSXMLVISTA Infrared Camera
Very Large Telescope
Versa Module Europa
Virtual Network Computing
Virtual Observatory
Volume Phased Hologram
Volume Phase Holographic Grating
Work Breakdown Structure
WFMOS Control SystemWFMOS WCS Electronics
Wide Field Corrector
Wide-field camera (IR camera for UKIRT)
Wide Field Fiber-Fed Optical Multi-Object Spectrometer
Wavefront Sensor
William Herschel Telescope
Work MonthWilkinson Microwave Anisotropy Probe
World Wide Web
Work YearAn intermediate dispersion spectrograph for the WHTExtensible Markup Language

141