1. 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

2. “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)

3. 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

4. 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

5. Imaging Surveys Survey Sq. Degrees FiltersDepthDatesStatus CTIO751shallowpublished VIRMOS91moderatepublished COSMOS2 (space)1moderatecomplete DLS (NOAO) 364deepcomplete Subaru30?1?deep 2005? observing CFH Legacy 1705moderate 2004-2008 observing RCS2 (CFH) 8303shallow 2005-2007 approved VST/KIDS/ VISTA/VIKING 17004+5moderate 2007-2010? 50%approved DES (NOAO) 50004moderate 2008-2012? proposed Pan-STARRS ~10,000?5?moderate 2006-2012? ~funded LSST15,000?5?deep 2014-2024? proposed JDEM/SNAP 1000+ (space) 9deep 2013-2018? proposed VST/VISTA DUNE 5000? 2010-2015? moderate 4+5 proposed 20000? (space) 2+1? moderate 2012-2018? proposed Y. Y. Mellier

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

7. The Dark Energy Survey

8. 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 2010-2015 (525 nights) Blanco 4-meter at CTIO 300,000,000 photometric redshifts within a volume of 23 (Gpc/h)^3, out to z = 2

9. 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

10. DES Status Low-risk, near-term (2010-15) 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

11. 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

12. 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

13. 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)

14. Photometric redshifts Probe strong spectral features (e.g. 4000 break) z=3.7z=0.1

15. 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 =10 5 -10 6

16. 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,…

17. 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”

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

19. ANNz - Artificial Neural Network Output: redshift Input: magnitudes Collister & Lahav 2004 http://www.star.ucl.ac.uk/~lahav/annz.html

20. 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 = 0.046 Collister, OL et al.

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

22. photo-z bins Collister et al.

23. Baryon oscillations from MegaZ-LRG Blake, Collister, Bridle & OL; astro-ph/0605303

24. Halo fit to MegaZ-LRG Blake, Collister,OL 0704.3377

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

26. The Dark Energy Survey

27. 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)

28. 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.

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

30. 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) 

31. 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

32. Optical and Optical+NIR Abdalla, Amara, Capak, Cypriano, OL, Rodes astro-ph/0705.1437

33. DE FoM for DUNE with and without NIR NIR will improve FoM by 1.3-1.7

34. DE FOM vs number of spectra needed Abdalla et al.

35. 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”

36. The END