Photo-z for LRGs, DES, DUNE and the cross talk with Dark Energy Ofer Lahav, University College London 1. The Dark Energy Survey 2. Photo-z methodology 3. Photo-z and probes 4. Applications: LRGs, DES, DUNE mainly with Filipe Abdalla and Manda Banerji Ofer Lahav University College London

“Evidence” for Dark Energy Observational data Type Ia Supernovae Galaxy Clusters Cosmic Microwave Background Large Scale Structure Gravitational Lensing Physical effects: Geometry Growth of Structure Both depend on the Hubble expansion rate: H 2 (z) = H 2 0 [  M (1+z) 3 +  DE (1+z) 3 (1+w) ] (flat)

Dark Energy: back to Newton? F = -GM/r 2 +  /3 r X * “I have now explained the two principle cases of attraction… which is very remarkable” Newton, Principia

The Future of the Local Universe  m =0.3 LCDM a = 1 (t= 13.5 Gyr) OCDM a = 1 (t= 11.3 Gyr) LCDM a = 6 (t= 42.4 Gyr) OCDM a = 6 (t= 89.2 Gyr) Hoffman, OL, Yepes & Dover 2007

Imaging Surveys Survey Sq. Degrees FiltersDepthDatesStatus CTIO751shallowpublished VIRMOS91moderatepublished COSMOS2 (space)1moderatecomplete DLS (NOAO) 364deepcomplete Subaru30?1?deep 2005? observing CFH Legacy 1705moderate observing RCS2 (CFH) 8303shallow approved VST/KIDS/ VISTA/VIKING moderate ? 50%approved DES (NOAO) 50004moderate ? proposed Pan-STARRS ~10,000?5?moderate ? ~funded LSST15,000?5?deep ? proposed JDEM/SNAP (space) 9deep ? proposed VST/VISTA DUNE 5000? ? moderate 4+5 proposed 20000? (space) 2+1? moderate ? proposed Y. Y. Mellier

Photo-z / Cosmology Synergy Large Scale Structure Clusters of Galaxies Simulations Photo-z Gravitational Lensing

The Dark Energy Survey

Study Dark Energy using 4 complementary techniques: I. Cluster Counts II. Weak Lensing III. Baryon Acoustic Oscillations IV. Supernovae Two multi-band surveys 5000 deg 2 g, r, i, z 40 deg 2 repeat (SNe) Build new 3 deg 2 camera and data management system Survey (525 nights) Blanco 4-meter at CTIO 300,000,000 photometric redshifts within a volume of 23 (Gpc/h)^3, out to z = 2

DES Organization Supernovae B. Nichol J. Marriner Clusters J. Mohr T. McKay Weak Lensing B. Jain S. Bridle Galaxy Clustering E. Gaztanaga W. Percival Photometric Redshifts F. Castander H. Lin Simulations A.Kravtsov A. Evrard Science Working Groups DES:UK consortium: UCL, Portsmouth, Cambridge, Edinburgh, Sussex Over 100 scientists in 17 institutions In the US, UK, Spain and Brazil

DES Status Low-risk, near-term ( ) project with high discovery potential Survey strategy delivers substantial DE science after 2 years Synergy with SPT and VISTA Precursor to LSST, DUNE and JDEM Total cost is relatively modest (~ $20-30M)  STFC approved £1.7M for the DES optical corrector, subject to funding in the US  Glass ordered by UCL in Sep 07 (funds from 5 universities)  DES in the US President budget request for FY08  DOE CD1 approved; CD2/CD3 in Jan 08  NSF contribution to data management

DES Forecast Constraints DES+Stage 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 haven’t made use of all the information DETF FoM

DES Forecasts: Power of Multiple Techniques FoM factor 4.6 tigther compared to near term projects w(z) =w 0 +w a (1–a) 68% CL Ma, Tang, Weller

Sources of uncertainties in measuring Dark Energy Theoretical (e.g. the cosmological model) Astrophysical (e.g. galaxy and cluster properties) Instrumental (e.g. image quality)

Photometric redshifts Probe strong spectral features (e.g break) z=3.7z=0.1

Photo-z –Dark Energy cross talk Approximately, for a photo-z slice: (  w/ w) = 5 (  z/ z) = 5 (  z /z) N s -1/2 => the target accuracy in w and photo-z scatter  z dictate the number of required spectroscopic redshifts N s =

Cosmology from photo-z surveys Optimization of Photo-z for cosmic probes Photo-z mocks and algorithms Spetroscopic training sets MegaZ-LRG (DR6) DES VISTA DUNE other surveys BAO, WL, neutrino mass, ISW, halo parameters,…

Photo-z Challenges Optimizing hybrid methods - errors - pdf - ‘clippping’ Optimal filters Spetroscopic training sets Field vs cluster photo-z Synergy with BAO and WL “Self calibration” and “colour tomography”

Photo-z Methods Template fitting (e.g. Hyper-z) Bayesian methods (e.g. BPZ, Zebra) Training-based methods (e.g. ANNz)

ANNz - Artificial Neural Network Output: redshift Input: magnitudes Collister & Lahav

MegaZ-LRG *Training on ~13,000 2SLAQ *Generating with ANNz Photo-z for ~1,000,000 LRGs over 5,000 sq deg, 2.5 (Gpc/h)^3  z = Collister, OL et al.

LRG - photo-z code comparison M. Banerji, F. Abdalla F., V. Rashkov, OL et al

photo-z bins Collister et al.

Baryon oscillations from MegaZ-LRG Blake, Collister, Bridle & OL; astro-ph/

Halo fit to MegaZ-LRG Blake, Collister,OL

Excess Power on Large Scales? Blake et al. 06 Padmanabhan et al. 06

The Dark Energy Survey

DES+VISTA would improve photo-z by a factor of 2 for z> 1 What is the effect on WL, BAO, SNIa Science? Banerji, Abdalla, OL, Lin et al. DES (5 filters) vs. DES+VISTA(8 filters)

DES+VISTA: Galaxy Power Spectrum DES grizY DES grizY + VISTA JHK For the same clipping threshold, we can measure the power spectrum accurately to higher redshifts using the DES+VISTA data.

DES+VISTA : Effect of Reddening Plots generated using JPL mocks (P.Capak) which include the effects of reddening

DES z=0.8 photo-z shell Back of the envelope: improved by sqrt (volume) => Sub-eV from DES (OL, Abdalla, Black, Kiakotou; in prep) 

DUNE: Dark UNiverse Explorer Mission baseline: 1.2m telescope FOV 0.5 deg 2 PSF FWHM 0.23’’ Pixels 0.11’’ GEO (or HEO) orbit Surveys (3-year initial programme): WL survey: 20,000 deg 2 in 1 red broad band, 35 galaxies/amin 2 with median z ~ 1, ground based complement for photo-z’s Near-IR survey (J,H). Deeper than possible from ground. Secures z > 1 photo-z’s

Optical and Optical+NIR Abdalla, Amara, Capak, Cypriano, OL, Rodes astro-ph/

DE FoM for DUNE with and without NIR NIR will improve FoM by

DE FOM vs number of spectra needed Abdalla et al.

Photo-z Challenges Optimizing hybrid methods - errors - pdf - ‘clippping’ Optimal filters Spetroscopic training sets Field vs cluster photo-z Synergy with BAO and WL “Self calibration” and “colour tomography”

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