The Dark Energy Survey

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The Dark Energy Survey

• Josh Frieman
• Fermilab, May 1, 2008

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Dark Energy and Dark Matter
For a review, see JF, Turner, Huterer,
arXiv0803.0982 ARAA, in press
To gain understanding, we must carry out new
experiments and surveys to probe the nature of
Dark matter and Dark Energy
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What is the Nature of Dark Energy?

• Stress-Energy
• Gravity
• Key Experimental Questions
• Is DE observationally distinguishable from a
  cosmological
• constant, for which T?? (vacuum)
  ?g??/8?G, i.e., w 1?
• Can we distinguish between gravity and
  stress-energy?
• Combine geometric with structure-growth
  probes
• Does dark energy evolve ww(z)?

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What is the nature of Dark Energy?

• Probe dark energy through the history of the
  expansion rate
• and the growth of large-scale structure
• Four Primary Probes (DETF)
• Weak Lensing cosmic shear Distance
  r(z)growth
  
• Supernovae
  Distance
• Baryon Acoustic Oscillations
  DistanceH(z)
• Cluster counting
  Distancegrowth

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Model Assumptions

• Most current data analyses assume a simplified,
  two-parameter class of models
• Future experiments aim to constrain (at least)
  4-parameter models
• Higher-dimensional EOS parametrizations possible
• Other descriptions possible (e.g., kinematic)

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Current Constraints on Constant Dark Energy
Equation of State 2-parameter model Data
consistent with w?1?0.1 Allen et al 07
Kowalski et al 08
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Curvature and Dark Energy

• WMAP3
• SDSS2dFSN
• w(z)constant
• 3-parameter model
• Spergel etal 07

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Much weaker current constraints on Time-varying
Dark Energy 3-parameter model marginalized
over ?m Kowalski et al 08
Assumes flat Universe
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Dark Energy Task Force Report (2006)

• Defined Figure of Merit to compare expts and
  methods
• Highlighted 4 probes SN, WL, BAO, CL
• Envisioned staged program of experiments
• Stage II on-going or funded as of 2006
• Stage III intermediate in scale time
• Stage IV longer-term, larger scale
• LSST, JDEM

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Much weaker current constraints on Time-varying
Dark Energy 3-parameter model marginalized
over ?m Kowalski et al 08
Theoretical prejudice
Stage III
Stage IV
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The Dark Energy Survey

• Study Dark Energy using
• 4 complementary techniques
• I. Cluster Counts
• II. Weak Lensing
• III. Baryon Acoustic Oscillations
• IV. Supernovae
• Two multiband surveys
• 5000 deg2 g, r, i, z, Y to 24th mag
• 9 deg2 repeat (SNe)
• Build new 3 deg2 camera
• and Data management sytem
• Survey 2011-2016 (525 nights)
• Response to NOAO AO

Blanco 4-meter at CTIO
in systematics in cosmological parameter
degeneracies geometricstructure growth test
Dark Energy vs. Gravity
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The DES Collaboration
Fermilab University of Illinois at
Urbana-Champaign University of Chicago Lawrence
Berkeley National Lab NOAO/CTIO DES Spain
Consortium DES United Kingdom Consortium Universit
y of Michigan Ohio State University University of
Pennsylvania DES Brazil Consortium Argonne
National Laboratory
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The DES Instrument DECam
F8 Mirror
Filters Shutter
3556 mm
CCD Read out
Hexapod
Optical Lenses
1575 mm
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DES CCDs

• 62 2kx4k fully depleted CCDs
• 520 Megapixels
• Excellent red sensitivity
• Developed by LBNL

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Dark Energy Survey South Pole Telescope
10-m South Pole Telescope 4000 sq. deg. SZE
Survey
Blanco 4-m Optical Telescope at CTIO 5000 sq.
deg. Dark Energy Survey
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Synergy with VISTA Hemisphere Survey
ESO Public Survey late 2008 JHK imaging over
DES area
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Probing Dark Energy

• Primary Techniques identified by the
• Dark Energy Task Force report
• Supernovae
• Galaxy Clusters
• Weak Lensing
  
• Baryon Acoustic Oscillations
• Multiple Techniques needed complementary in
  systematics
• and in science reach

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Clusters and Dark Energy
Number of clusters above observable mass threshold

• Requirements
• Understand formation of dark matter halos
• Cleanly select massive dark matter halos (galaxy
  clusters) over a range of redshifts
• Redshift estimates for each cluster
• Observable proxy that can be used as cluster mass
  estimate
• p(OM,z)
• Primary systematic
• Uncertainty in bias scatter of mass-observable
  relation

Dark Energy equation of state

Mohr
Volume Growth (geometry)
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w 1
Growth of Density Perturbations
Volume Element
Flat, matter-dominated

• w -0.7

Raising w at fixed WDE decreases growth rate of
density perturbations and decreases volume
surveyed
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Clusters and Dark Energy
Number of clusters above observable mass threshold

• Requirements
• Understand formation of dark matter halos
• Cleanly select massive dark matter halos (galaxy
  clusters) over a range of redshifts
• Redshift estimates for each cluster
• Observable proxy that can be used as cluster mass
  estimate
• p(OM,z)
• Primary systematic
• Uncertainty in bias scatter of mass-observable
  relation

Dark Energy equation of state

Mohr
Volume Growth (geometry)
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Clusters form hierarchically
z 7
z 5
z 3
dark matter
time
z 0.5
z 0
z 1
Kravtsov
5 Mpc
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Theoretical Abundance of Dark Matter Halos
Warren etal
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Clusters and Dark Energy
Number of clusters above observable mass threshold

• Requirements
• Understand formation of dark matter halos
• Cleanly select massive dark matter halos (galaxy
  clusters) over a range of redshifts
• Redshift estimates for each cluster
• Observable proxy that can be used as cluster mass
  estimate
• p(OM,z)
• Primary systematic
• Uncertainty in bias scatter of mass-observable
  relation

Dark Energy equation of state

Mohr
Volume Growth (geometry)
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Cluster Selection

• 4 Techniques for Cluster Selection
• Optical galaxy concentration
• Weak Lensing
• Sunyaev-Zeldovich effect (SZE)
• X-ray

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10-m South Pole Telescope (SPT)

• Sunyaev-Zeldovich effect (SZE)
• Compton upscattering of CMB photons
• by hot gas in clusters
• - nearly independent of redshift
• - can probe to high redshift
• - need ancillary redshift measurement from
  DES

DES survey area encompasses 4000 sq. deg. SPT
SZE Survey Survey SPT now taking data
PI J. Carlstrom (U. Chicago)
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Holder
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Clusters and Dark Energy
Number of clusters above observable mass threshold

• Requirements
• Understand formation of dark matter halos
• Cleanly select massive dark matter halos (galaxy
  clusters) over a range of redshifts
• Redshift estimates for each cluster
• Observable proxy that can be used as cluster mass
  estimate
• p(OM,z)
• Primary systematic
• Uncertainty in bias scatter of mass-observable
  relation

Dark Energy equation of state

Mohr
Volume Growth (geometry)
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Photometric Redshifts
Elliptical galaxy spectrum
Measure relative flux in multiple
filters track the 4000 A break Estimate
individual galaxy redshifts with accuracy
?(z) lt 0.1 (0.02 for clusters) Precision is
sufficient for Dark Energy probes,
provided error distributions well
measured. Good detector response in z
band filter needed to reach zgt1

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Galaxy Photo-z Simulations
VHS
DES griz
DES
DES griZY VHS JHKs on ESO VISTA 4-m enhances
science reach
10? Limiting Magnitudes g 24.6 r 24.1
i 24.0 z 23.9 2 photometric
calibration error added in quadrature Key
Photo-z systematic errors under control using
existing spectroscopic training sets to DES
photometric depth low-risk
J 20.3 H 19.4 Ks 18.3
Z 23.8 Y 21.6
Vista Hemisphere Survey
Developed improved Photo-z Error Estimates and
robust methods of outlier rejection Oyaizu,
Cunha, Lima, Frieman, Lin
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Bias
Variance and Bias of Photo-z Estimates
Variance
Cunha etal
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Spectroscopic Redshift Training Sets for DES
Training Sets to the DES photometric depth
largely in place (advantage of a relatively
shallow survey)
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Clusters and Dark Energy
Number of clusters above observable mass threshold

• Requirements
• Understand formation of dark matter halos
• Cleanly select massive dark matter halos (galaxy
  clusters) over a range of redshifts
• Redshift estimates for each cluster
• Observable proxy that can be used as cluster mass
  estimate
• p(OM,z)
• Primary systematic
• Uncertainty in bias scatter of mass-observable
  relation

Dark Energy equation of state

Mohr
Volume Growth (geometry)
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Precision Cosmology with Clusters?

• Effect of
• Uncertainty in
• mass-observable
• relation

Mass threshold
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Cluster Mass Estimates

• 4 Techniques for Cluster Mass Estimation
• Optical galaxy concentration
• Weak Lensing
• Sunyaev-Zeldovich effect (SZE)
• X-ray
• Cross-compare these techniques to reduce
  systematic errors
• Additional cross-checks
• shape of mass function cluster
• correlations

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SZE vs. Cluster Mass Progress toward Realistic
Simulations
? Adiabatic ? CoolingStar Formation
SZE flux
small (10) scatter

• SZE Observable

Kravtsov
Nagai
Integrated SZE flux decrement depends only on
cluster mass insensitive to details of gas
dynamics/galaxy formation in the cluster core
robust scaling relations
Motl, etal
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Weak Lensing of Faint Galaxies distortion of
shapes
Background Source shape
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Weak Lensing of Faint Galaxies distortion of
shapes
Foreground Cluster
Background Source shape
Note the effect has been greatly exaggerated here
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Lensing of real (elliptically shaped) galaxies
Foreground Cluster
Background Source shape
Co-add signal around a number of Clusters
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Mapping the Dark Matter in a Cluster of
Galaxies via Weak Gravitational Lensing Data
from Blanco 4-meter at CTIO Joffre, etal
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Statistical Weak Lensing by Galaxy Clusters
Mean Tangential Shear Profile in Optical Richness
(Ngal) Bins to 30 h-1Mpc Sheldon, Johnston,
etal SDSS
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Statistical Weak Lensing by Galaxy Clusters
Mean Tangential Shear Profile in Optical Richness
(Ngal) Bins to 30 h-1Mpc Johnston, Sheldon,
etal SDSS
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Statistical Weak Lensing Calibrates Cluster Mass
vs. Observable Relation
Cluster Mass vs. Number of galaxies they
contain Future use this to independently
calibrate, e.g., SZE vs. Mass
SDSS Data zlt0.3
Statistical Lensing eliminates projection
effects of individual cluster mass estimates
Johnston, Sheldon, etal
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Probing Dark Energy

• Primary Techniques identified by the
• Dark Energy Task Force report
• Supernovae
• Galaxy Clusters
• Weak Lensing
  
• Baryon Acoustic Oscillations
• Multiple Techniques needed complementary in
  systematics
• and in science reach

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Background sources
Dark matter halos
Observer

• Statistical measure of shear pattern, 1
  distortion
• Radial distances depend on geometry of Universe
• Foreground mass distribution depends on growth of
  structure

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Weak lensing shear and mass
Jain
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Weak Lensing Tomography

• Cosmic Shear
• Angular Power
• Spectrum in 4
• Photo-z Slices
• Shapes of 3x108 galaxies
• ?z?0.7
• Primary Systematics
• photo-zs, PSF anisotropy,
• shear calibration

Statistical errors shown
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Understanding Improving Blanco Image Quality
Mosaic II an observed PSF pattern

• Ray tracing model of Mosaic correctorprimary
• Reproduces PSF patterns qualitatively explains
  PCA components
• Focus errors coupled to primary astigmatism
• MisalignmentADC induces coma
• Guiding errors
• Trefoil distortions of primary due to mirror
  supports
• Improvements for DES
• New corrector with small smoothly
• varying PSF
• active focus sensors on focal plane
• wavefront (curvature) sensors
• active control of prime focus cage tilt
• active control of primary (low order)
• fix radial support system
• improve thermal environment
• (reduce local power dissipation)

Mosaic II optical model ADC - neutral position
axis to right Primary misaligned
by 0.2 mm x -0.7 mm y results in Coma
Offsets of this amount have been measured
due in part to broken radial supports
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Weak Lensing Tomography
Huterer etal

• Cosmic Shear Angular Power Spectrum in
• Photo-z Slices
• Shapes of 300 million well-resolved galaxies,
• ?z? 0.7
• Primary Systematics
• photo-zs,
• PSF anisotropy,
• shear calibration
• Extra info in bispectrum galaxy-shear robust

Statistical errors shown
DES WL forecasts conservatively assume 0.9 PSF
median delivered to existing Blanco camera DES
should do better be more stable
DES WL forecasts conservatively assume 0.9 PSF
median delivered to existing Blanco camera
DECam should do better be more stable
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Weak Lensing Photo-z Systematics
?(w0)/?(w0pz fixed)
?(wa)/?(wapz fixed)
Ma
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Theory Uncertainty in P(k) and WL

• WL data can be used to self-calibrate baryon
  impact on small-scale Power spectrum
• Zentner, Rudd, Hu

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Probing Dark Energy

• Primary Techniques identified by the
• Dark Energy Task Force report
• Supernovae
• Galaxy Clusters
• Weak Lensing
  
• Baryon Acoustic Oscillations
• Multiple Techniques needed complementary in
  systematics
• and in science reach

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Baryon Acoustic Oscillations (BAO) in the CMB

• Characteristic angular scale set by sound horizon
  at recombination standard ruler (geometric
  probe).

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Sound Waves in the Early Universe

• Before recombination
• Universe is ionized.
• Photons provide enormous pressure and restoring
  force.
• Perturbations oscillate as acoustic waves.

• After recombination
• Universe is neutral.
• Photons can travel freely past the baryons.
• Phase of oscillation at trec affects late-time
  amplitude.

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Sound Waves

• Each initial overdensity (in dark matter gas)
  is an overpressure that launches a spherical
  sound wave.
• This wave travels outwards at 57 of the speed
  of light.
• Pressure-providing photons decouple at
  recombination. CMB travels to us from these
  spheres.
• Sound speed plummets. Wave stalls at a radius of
  150 Mpc.
• Overdensity in shell (gas) and in the original
  center (DM) both seed the formation of galaxies.
  Preferred separation of 150 Mpc.

Eisenstein
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A Statistical Signal

• The Universe is a super-position of these shells.
• The shell is weaker than displayed.
• Hence, you do not expect to see bullseyes in the
  galaxy distribution.
• Instead, we get a 1 bump in the correlation
  function.

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Baryon Acoustic Oscillations CMB Galaxies
Acoustic series in P(k) becomes a single peak in
?(r)
CMB Angular Power Spectrum
SDSS galaxy correlation function
Bennett, etal
Eisenstein etal
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BAO in DES Galaxy Angular Power Spectrum
Wiggles due to BAO
Systematics photo-zs, correlated photometric
errors, non-linearity, scale-dependent bias
Blake Bridle
Fosalba Gaztanaga
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Probing Dark Energy

• Primary Techniques identified by the
• Dark Energy Task Force report
• Supernovae
• Galaxy Clusters
• Weak Lensing
  
• Baryon Acoustic Oscillations
• Multiple Techniques needed complementary in
  systematics
• and in science reach

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SN Ia Hubble Diagram
Wood-Vasey et.al 2007

• Primary Systematics
• dust extinctionSN colors, population
  evolution

184 'Gold' SNe Ia Riess et.al 2006
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IV. Supernovae

• Geometric Probe of Dark Energy
• Baseline use 10 of photometric time, most of
  the non-photometric time 5 visits per lunation
  in riz
• 900-4000 well-measured SN Ia
• lightcurves to z1
• Larger sample, improved z-band response (fully
  depleted CCDs) compared to ESSENCE, SNLS reduce
  dependence on rest-frame u-band and Malmquist
  bias
• Spectroscopic follow-up of SN subsamplehost
  galaxies (LBT, Magellan, Gemini, Keck, VLT,)
  e.g., focus on ellipticals (low dust extinction)

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Probing Dark Energy

• Probe dark energy through the history of the
  expansion rate
• and the growth of large-scale structure
• Parametrize DE Evolution
  
• Geometric tests
• Comoving distance Weak Lensing
  
• Standard Candles Supernovae
  
• Standard Rulers Baryon Oscillations
• Standard Population Clusters

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DES Forecasts Power of Multiple Techniques
w(z) w0wa(1a) 68 CL

Assumptions Clusters ?80.75, zmax1.5, WL
mass calibration BAO lmax300 WL
lmax1000 (no bispectrum) Statisticalphoto-z
systematic errors only Spatial curvature,
galaxy bias marginalized, Planck CMB prior

DETF Figure of Merit inverse area of ellipse

• geometric
• growth

Stage II not included here
geometric

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Forecast Constraints
DETF FoM

• DESStage II combined Factor 4.6 improvement
  over Stage II combined
• Consistent with DETF range for Stage III DES-like
  project
• Large uncertainties in systematics remain, but
  FoM is robust to uncertainties
• in any one probe, and we havent made use of
  all the information

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Conclusion

• DES is poised to take the next step in probing
  the Dark Energy, aiming toward greater
  understanding of the physics of cosmic
  acceleration.
• Complementary with BOSS (spectroscopic BAO)
• Using multiple techniques to probe Dark Energy is
  important to control systematic errors and
  achieve robust constraints.
• A fruitful international collaboration that will
  produce a rich public legacy data set for years
  to come.