Frieman DES CD1

The Dark Energy Survey in Context: 

The Dark Energy Survey in Context Josh Frieman Fermilab and University of Chicago White Papers submitted to Dark Energy Task Force: astro-ph/0510346 Theoretical & Computational Challenges: astro-ph/0510194,5

The Dark Energy Survey: 

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 40 deg2 repeat (SNe) • Build new 3 deg2 camera and Data management sytem Survey 2009-2015 (525 nights) Response to NOAO AO Blanco 4-meter at CTIO *in systematics & in cosmological parameter degeneracies *geometric+structure growth: test Dark Energy vs. Gravity

Dark Energy Task Force Report: 

Dark Energy Task Force Report Established by AAAC and HEPAP as joint subcommittee to advise the 3 agencies ``Strongly recommend…an aggressive program to explore dark energy” Considered 4 main techniques to study DE (those above) Defined stages of projects: Stage I=completed; II=on-going; III=near-term, medium-cost, proposed; IV=LST, SKA, JDEM ``Recommend that the…program have multiple techniques at every stage” DETF Stage III: 4-m telescope: BAO photo-z, clusters w/ SZE, SNe , WL, i.e., DES; and 8-m spectroscopic BAO (WFMOS) Recommend immediate start of Stage III Defined a Figure of Merit for comparing DE projects (see below)

Photometric Redshifts: 

Photometric Redshifts • 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 Elliptical galaxy spectrum

Slide5: 

DES griz filters 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 Galaxy Photo-z Simulations +VDES JK Developed improved Photo-z & Error Estimates and robust methods of outlier rejection DES DES + VDES on ESO VISTA 4-m enhances science reach

I. Clusters and Dark Energy: 

I. Clusters and Dark Energy Mohr Volume Growth Number of clusters above observable mass threshold Dark Energy equation of state 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: O =g(M) Primary systematic: Uncertainty in bias & scatter of mass-observable relation

Cluster Cosmology with DES : 

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

10-m South Pole Telescope (SPT): 

10-m South Pole Telescope (SPT) SPT will carry out 4000 sq. deg. SZE Survey PI: J. Carlstrom (U. Chicago) NSF-OPP funded & scheduled for Nov 2006 deployment DOE (LBNL) funding of readout development 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 Dec 2005

SZE vs. Cluster Mass: Progress in Realistic Simulations: 

SZE vs. Cluster Mass: Progress in Realistic Simulations Motl, etal Integrated SZE flux decrement depends only on cluster mass: insensitive to details of gas dynamics/galaxy formation in the cluster core robust scaling relations Nagai SZE flux  Adiabatic ∆ Cooling+Star Formation SPT Observable Kravtsov small (~10%) scatter

Slide10: 

Statistical Weak Lensing Calibrates Cluster Mass vs. Observable Relation Cluster Mass vs. Number of galaxies they contain For DES, will use this to independently calibrate SZE vs. Mass Johnston, Sheldon, etal, in preparation Statistical Lensing eliminates projection effects of individual cluster mass estimates Johnston, etal astro-ph/0507467 SDSS Data Preliminary z<0.3

Slide11: 

Observer Dark matter halos Background sources Statistical measure of shear pattern, ~1% distortion Radial distances depend on geometry of Universe Foreground mass distribution depends on growth of structure

Slide12: 

Cosmic Shear Angular Power Spectrum in 4 Photo-z Slices Shapes of ~300 million galaxies, median redshift z = 0.7 Primary Systematics: photo-z’s, PSF anisotropy, shear calibration Weak Lensing Tomography DES WL forecasts conservatively assume 0.9” PSF = median delivered to existing Blanco camera: DECam should do better & be more stable Huterer Statistical errors shown

Reducing WL Shear Systematics: 

Reducing WL Shear Systematics DECam+Blanco hardware improvements will further reduce raw lensing systematics Red: expected signal Results from 75 sq. deg. WL Survey with Mosaic II and BTC on the Blanco 4-m Bernstein, etal DES: comparable depth: source galaxies well resolved & bright: low-risk (improved systematic) (signal) Believe shear systematics under control at level needed for DES (old systematic) Cosmic Shear

III. Baryon Acoustic Oscillations (BAO) in the CMB: 

III. Baryon Acoustic Oscillations (BAO) in the CMB Characteristic angular scale set by sound horizon at recombination: standard ruler (geometric probe).

Baryon Acoustic Oscillations: CMB & Galaxies: 

Baryon Acoustic Oscillations: CMB & Galaxies CMB Angular Power Spectrum SDSS galaxy correlation function Acoustic series in P(k) becomes a single peak in (r) Bennett, etal Eisenstein etal

Slide16: 

BAO in DES: Galaxy Angular Power Spectrum Probe larger volume and redshift range than SDSS Systematics: photo-z’s, photometric errors Wiggles due to BAO Blake & Bridle Fosalba & Gaztanaga

IV. Supernovae: 

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 Improved photometric precision via in-situ photometric response measurements SDSS

DES Forecasts: Power of Multiple Techniques: 

DES Forecasts: Power of Multiple Techniques Ma, Weller, Huterer, etal Assumptions: Clusters: 8=0.75, zmax=1.5, WL mass calibration (no clustering) BAO: lmax=300 WL: lmax=1000 (no bispectrum) Statistical+photo-z systematic errors only Spatial curvature, galaxy bias marginalized Planck CMB prior w(z) =w0+wa(1–a) 68% CL geometric geometric+ growth Clusters if 8=0.9 DETF Figure of Merit: inverse area of ellipse

Slide19: 

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: factor 3-5 improvement in DETF FOM Scientific and technical precursor to the more ambitious Stage IV Dark Energy projects to follow DES in unique international position to synergize with SPT (and VISTA) on the DETF Stage III timescale DES and the Dark Energy Program

Extra Slides: 

Extra Slides

Bias: 

Variance and Bias of Photo-z Estimates Cunha etal Variance Bias

Clusters and Photo-z Systematics: 

Clusters and Photo-z Systematics

Weak Lensing & Photo-z Systematics: 

Weak Lensing & Photo-z Systematics Ma (w0)/(w0|pz fixed) (wa)/(wa|pz fixed)

BAO & Photo-z Systematics: 

BAO & Photo-z Systematics Ma (w0)/(w0|pz fixed) (wa)/(wa|pz fixed)

Supernovae and photo-z errors: 

Supernovae and photo-z errors Huterer

Forecasts for Constant w Models: 

Forecasts for Constant w Models (DE) (w))

Forecasts with WMAP Priors: 

Forecasts with WMAP Priors (w0) (wa)

Slide28: 

Survey Power (approximate) Partial Source: Pan-STARRS Website