Transcript The Dark Energy Survey Josh Frieman White Papers submitted to Dark Energy Task Force: astro-ph/0510346 Theoretical & Computational Challenges: astro-ph/0510194,5 P5 – April 20, 2006

The Dark Energy Survey
Josh Frieman
White Papers submitted to
Dark Energy Task Force:
astro-ph/0510346
Theoretical & Computational
Challenges:
astro-ph/0510194,5
P5 – April 20, 2006
1
The Dark Energy Survey
• Study Dark Energy using
4 complementary* techniques:
Blanco 4-meter at CTIO
I. Cluster Counts
II. Weak Lensing
III. Baryon Acoustic Oscillations
IV. Supernovae
•
Two multiband surveys:
5000 deg2 g, r, i, z
40 deg2 repeat (SNe)
•
Build new 3 deg2 camera
and Data management sytem
Survey 2009-2015 (525 nights)
*in systematics & in cosmological parameter degeneracies
Response to NOAO AO
*geometric+structure growth: test Dark Energy vs. Gravity
P5 – April 20, 2006
2
The DES Collaboration
Fermilab: J. Annis, H. T. Diehl, S. Dodelson, J. Estrada, B. Flaugher, J. Frieman,
S. Kent, H. Lin, P. Limon, K. W. Merritt, J. Peoples, V. Scarpine, A. Stebbins,
C. Stoughton, D. Tucker, W. Wester
University of Illinois at Urbana-Champaign: C. Beldica, R. Brunner, I. Karliner,
J. Mohr, R. Plante, P. Ricker, M. Selen, J. Thaler
University of Chicago: J. Carlstrom, S. Dodelson, J. Frieman, M. Gladders,
W. Hu, S. Kent, R. Kessler, E. Sheldon, R. Wechsler
Lawrence Berkeley National Lab: N. Roe, C. Bebek, M. Levi, S. Perlmutter
University of Michigan: R. Bernstein, B. Bigelow, M. Campbell, D. Gerdes, A. Evrard,
W. Lorenzon, T. McKay, M. Schubnell, G. Tarle, M. Tecchio
NOAO/CTIO: T. Abbott, C. Miller, C. Smith, N. Suntzeff, A. Walker
CSIC/Institut d'Estudis Espacials de Catalunya (Barcelona): F. Castander, P.
Fosalba, E. Gaztañaga, J. Miralda-Escude
Institut de Fisica d'Altes Energies (Barcelona): E. Fernández, M. Martínez
CIEMAT (Madrid): C. Mana, M. Molla, E. Sanchez, J. Garcia-Bellido
University College London: O. Lahav, D. Brooks, P. Doel, M. Barlow, S. Bridle,
S. Viti, J. Weller
University of Cambridge: G. Efstathiou, R. McMahon, W. Sutherland
University of Edinburgh: J. Peacock
University of Portsmouth: R. Crittenden, R. Nichol, W. Percival
University of Sussex: A. Liddle, K. Romer
plus students
P5 - April 20, 2006
3
Photometric Redshifts
Elliptical galaxy spectrum
• Measure relative flux in
four filters griz:
track the 4000 A break
• Estimate individual galaxy
redshifts with accuracy
(z) < 0.1 (~0.02 for clusters)
• Precision is sufficient
for Dark Energy probes,
provided error distributions
well measured.
• Note: good detector response in
z band filter needed to reach z>1
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4
Galaxy Photo-z Simulations
DES +VDES JK
griz filters
10 Limiting Magnitudes
g
24.6
r
24.1
i
24.0
z
23.9
DES+ VDES on
DES
ESO VISTA 4-m
enhances science reach
+2% photometric calibration
error added in quadrature
Key: Photo-z systematic errors
under control using existing
spectroscopic training sets to
DES photometric depth
Improved Photo-z & Error Estimates and robust methods of outlier rejection
P5 – April 20, 2006
Cunha, etal
I. Clusters and Dark Energy
Number of clusters above observable mass threshold
•Requirements
1.Understand formation of dark
matter halos
2.Cleanly select massive dark matter
halos (galaxy clusters) over a range
of redshifts
3.Redshift estimates for each cluster
4.Observable proxy that can be used
as cluster mass estimate:
O =g(M)
Primary systematic:
Uncertainty in bias & scatter of
mass-observable relation
Dark Energy
equation of state
dN(z)
dV

n z
dzd dzd

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Volume
(geometry)
Growth
Mohr
6
Cluster Cosmology with DES
•
3 Techniques for Cluster Selection and
Mass Estimation:
• Optical galaxy concentration
• Weak Lensing
• Sunyaev-Zel’dovich effect (SZE)
• Cross-compare these techniques to
reduce systematic errors
• Additional cross-checks:
shape of mass function; cluster
correlations
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7
10-m South Pole Telescope (SPT)
Sunyaev-Zel’dovich effect
- Compton
upscattering of CMB photons
by hot gas in clusters
- nearly independent of redshift:
- can probe to high redshift
- need ancillary redshift measurement
SPT will carry out 4000 sq. deg. SZE
Survey
PI: J. Carlstrom (U. Chicago)
Dec 2005
NSF-OPP funded & scheduled for Nov 2006 deployment
DOE (LBNL) funding of readout development
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SPT Observable
SZE flux
SZE vs. Cluster Mass: Progress in
Realistic Simulations
 Adiabatic
∆ Cooling+Star
Formation
small (~10%)
scatter
Kravtsov
Future:
SCIDAC
proposal
Integrated SZE flux decrement depends only on cluster
mass: insensitive to details of gas dynamics/galaxy
formation in the cluster core
robust scaling relations
P5 – April 20, 2006
Nagai
Motl, etal
9
Statistical Weak Lensing Calibrates
Cluster Mass vs. Observable Relation
Cluster Mass
vs. Number
of galaxies they
contain
SDSS Data
Preliminary
z<0.3
Statistical
Lensing
eliminates
projection effects
of individual
cluster mass
estimates
For DES, will
use this to
independently
calibrate
SZE vs. Mass
Johnston, Sheldon, etal, in preparation
P5 – April 20, 2006
Johnston, etal
astro-ph/0507467
10
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 Tomography
•Cosmic Shear
Angular Power
Spectrum in 4
Photo-z Slices
•Shapes of ~300
million galaxies
Statistical errors
shown
median redshift z = 0.7
•Primary Systematics:
photo-z’s, PSF anisotropy,
shear calibration
Huterer
DES WL forecasts conservatively assume 0.9” PSF = median delivered to
existing Blanco camera: DES should do better & be more stable (see Brenna’s talk)
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Reducing WL Shear Systematics
Cosmic
Shear
(signal)
(old systematic)
(improved systematic)
Red: expected signal
Results from
75 sq. deg. WL
Survey with
Mosaic II and BTC
on the Blanco 4-m
Bernstein, etal
See Brenna’s talk
for
DECam+Blanco
hardware
improvements that
will reduce raw
lensing
systematics
DES: comparable
depth: source
galaxies well
resolved & bright:
low-risk
Shear systematics under control at level needed for DES
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III. Baryon Acoustic Oscillations (BAO) in the CMB

Characteristic angular scale set by sound horizon at
recombination: standard ruler (geometric probe).
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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
Probe substantially
larger volume and
redshift range than
SDSS
Fosalba & Gaztanaga
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Blake & Bridle
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IV. Supernovae
• Geometric Probe of Dark Energy
• Repeat observations of 40 deg2 , using
10% of survey time
• ~1900 well-measured SN Ia
lightcurves, 0.25 < z < 0.75
• Larger sample, improved z-band response
compared to ESSENCE, SNLS; address
issues they raise
• Improved photometric precision via insitu photometric response measurements
SDSS
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17
DES Forecasts: Power of Multiple Techniques
Assumptions:
Clusters:
8=0.75, zmax=1.5,
WL mass calibration
(no clustering)
w(z) =w0+wa(1–a)
68% CL
BAO: lmax=300
WL: lmax=1000
(no bispectrum)
Statistical+photo-z
systematic errors only
Spatial curvature, galaxy bias
marginalized
geometric+
growth
Clusters
if 8=0.9
geometric
Planck CMB prior
Ma, Weller,
Huterer, etal
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DES and a Dark Energy Program
•
Will measure Dark Energy using multiple complementary probes,
developing these techniques and exploring their systematic error
floors
•
Survey strategy delivers substantial DE science after 2 years
•
Relatively modest, low-risk, near-term project with high discovery
potential
•
Scientific and technical precursor to the more ambitious Stage IV
Dark Energy projects to follow: LSST and JDEM
•
DES in unique international position to synergize with SPT and
VISTA on the DETF Stage III timescale (PanSTARRS is in the
Northern hemisphere; cannot be done with existing facilities in the
South)
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Extra Slides
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Spectroscopic Redshift
Training Sets for DES
Redshift Survey
Number of Redshifts
Overlapping DES
Sloan Digital Sky Survey
70,000, r < 20
2dF Galaxy Redshift
Survey
90,000, bJ<19.45
VIMOS VLT Deep Survey
~60,000, IAB<24
DEEP2 Redshift Survey
~30,000, RAB<24.1
Training Sets to the DES photometric depth in place
(advantage of a `relatively’ shallow survey)
P5 – April 20, 2006
DES Cluster Photometric Redshift Simulations
DES:
for clusters,
(z) < 0.02 for z <1.3
DES+VDES
griz+JK on VISTA:
extend photo-z’s to
z~2
(enhances, but not
critical to, science
goals)
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Variance and Bias of Photo-z Estimates
Bias
Variance
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Cunha etal
Photo-z Error Distributions & Error Estimates
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Robustly Reducing Catastrophic Errors
Original
10% Cut
Remove 10% of objects via color cuts
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30% improvement
Clusters and Photo-z Systematics
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Weak Lensing & Photo-z Systematics
(w0)/(w0|pz fixed)
(wa)/(wa|pz fixed)
Ma
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BAO & Photo-z Systematics
(w0)/(w0|pz fixed)
(wa)/(wa|pz fixed)
Ma
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Supernovae and photo-z errors
Huterer
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29
Improving Corrections for Anisotropic PSF
Focus too low
Focus (roughly) correct
Focus too high
Whisker plots for three BTC camera exposures; ~10% ellipticity
 Left and right are most extreme variations, middle is more typical.
 Correlated variation in the different exposures: PCA analysis -->
can use stars in all the images: much better PSF interpolation

P5 - April 20, 2006
Jarvis and Jain
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PCA Analysis
Focus too low
Focus (roughly) correct
Focus too high
Remaining ellipticities are essentially uncorrelated.
 Measurement error is the cause of the residual shapes.
 1st improvement: higher order polynomial means PSF accurate to smaller scales
 2nd: Much lower correlated residuals on all scales

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Lensing Cluster
Source
Image
Tangential shear
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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 preliminary
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Invert Mean Shear Profile to obtain Mean Mass Profile
Virial radius
Virial Mass
P5 – April 20, 2006
Johnston,
Sheldon, etal
SDSS
preliminary
34
Precision Cosmology with Clusters
Sensitivity to Mass Threshold
•
Requirements
1.
2.
3.
4.
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:
O =g(M)
Primary systematic:
Uncertainty in bias & scatter of massobservable relation
P5 – April 20, 2006
dN(z)
dnM,z
2
2
c

d 1 z  dM
f M 
dM
dzd H z A
0
Mass
threshold
35
Forecasts for Constant w Models
(DE) (w)
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Forecasts with WMAP Priors
(w0) (wa)
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