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

P5 – April 20, 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 deg 2 g, r, i, z 40 deg 2 repeat (SNe) Build new 3 deg 2 camera and Data management sytem Survey (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

P5 - April 20, 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, 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

P5 – April 20, 2006 DES griz filters 10  Limiting Magnitudes g24.6 r24.1 i24.0 z % photometric calibration error added in quadrature Key: Photo-z systematic errors under control using existing spectroscopic training sets to DES photometric depth Galaxy Photo-z Simulations +VDES JK Improved Photo-z & Error Estimates and robust methods of outlier rejection DES Cunha, etal DES + VDES on ESO VISTA 4-m enhances science reach

P5 – April 20, I. Clusters and Dark Energy Mohr Volume Growth (geometry) Number of clusters above observable mass threshold Dark Energy equation of state 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

P5 – April 20, 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

P5 – April 20, 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

P5 – April 20, 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 Future: SCIDAC proposal small (~10%) scatter

P5 – April 20, 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/ SDSS Data Preliminary z<0.3

P5 – April 20, 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

P5 – April 20, 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: DES should do better & be more stable (see Brenna’s talk) Huterer Statistical errors shown

P5 - April 20, Reducing WL Shear Systematics See Brenna’s talk for DECam+Blanco hardware improvements that will 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) Shear systematics under control at level needed for DES (old systematic) Cosmic Shear

P5 - April 20, III. Baryon Acoustic Oscillations (BAO) in the CMB Characteristic angular scale set by sound horizon at recombination: standard ruler (geometric probe).

P5 - April 20, 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

P5 – April 20, BAO in DES: Galaxy Angular Power Spectrum Probe substantially larger volume and redshift range than SDSS Wiggles due to BAO Blake & BridleFosalba & Gaztanaga

P5 – April 20, IV. Supernovae Geometric Probe of Dark Energy Repeat observations of 40 deg 2, 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 in- situ photometric response measurements SDSS

P5 – April 20, DES Forecasts: Power of Multiple Techniques Ma, Weller, Huterer, etal Assumptions: Clusters:  8 =0.75, z max =1.5, WL mass calibration (no clustering) BAO: l max =300 WL: l max =1000 (no bispectrum) Statistical+photo-z systematic errors only Spatial curvature, galaxy bias marginalized Planck CMB prior w(z) =w 0 +w a (1–a) 68% CL geometric geometric+ growth Clusters if  8 =0.9

P5 – April 20, 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) DES and a Dark Energy Program

P5 – April 20, Extra Slides

P5 – April 20, 2006 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, b J <19.45 VIMOS VLT Deep Survey~60,000, I AB <24 DEEP2 Redshift Survey~30,000, R AB <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)

P5 – April 20, 2006 Variance and Bias of Photo-z Estimates Cunha etal Variance Bias

P5 – April 20, 2006 Photo-z Error Distributions & Error Estimates

P5 – April 20, 2006 Robustly Reducing Catastrophic Errors Remove 10% of objects via color cuts 30% improvement Original10% Cut

P5 – April 20, 2006 Clusters and Photo-z Systematics

P5 – April 20, Weak Lensing & Photo-z Systematics Ma  (w 0 )/  (w 0 |pz fixed)  (w a )/  (w a |pz fixed)

P5 – April 20, BAO & Photo-z Systematics Ma  (w 0 )/  (w 0 |pz fixed)  (w a )/  (w a |pz fixed)

P5 – April 20, Supernovae and photo-z errors Huterer

P5 - April 20, Improving Corrections for Anisotropic PSF 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 Focus too lowFocus (roughly) correctFocus too high Jarvis and Jain

P5 - April 20, PCA Analysis 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 Focus too low Focus (roughly) correctFocus too high

P5 – April 20, Image Lensing Cluster Source Tangential shear

P5 – April 20, Statistical Weak Lensing by Galaxy Clusters Mean Tangential Shear Profile in Optical Richness (N gal ) Bins to 30 h -1 Mpc Sheldon, Johnston, etal SDSS preliminary

P5 – April 20, Johnston, Sheldon, etal SDSS preliminary Invert Mean Shear Profile to obtain Mean Mass Profile Virial Mass Virial radius

P5 – April 20, Precision Cosmology with Clusters 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 Sensitivity to Mass Threshold Mass threshold

P5 – April 20, Forecasts for Constant w Models  (  DE )  (w)

P5 – April 20, Forecasts with WMAP Priors  (w 0 )  (w a )