1. Bayesian Photometric Redshifts (BPZ) Narciso Benítez 1,2 (2000) Narciso Benítez 1,2 et al. (2004) Dan Coe 1,2,3 et al. (2006) Johns Hopkins University 1 Instituto de Astrofísica de Andalucía 2 JPL/Caltech 3 Science Team Science Team

2. Photo-z Methods  Spectral Energy Distribution (SED) Template Fitting  Empirical Training Set (Neural Networks)  Spectral Energy Distribution (SED) Template Fitting  Empirical Training Set (Neural Networks)

3. Coleman, Wu, Weedman ‘80 Kinney ‘96 Bruzual & Charlot ‘03 Spectral Energy Distribution (SED) templates BPZ v1.99b Benítez ‘00, ‘04 Coe ‘06 recalibrated with real photometry http://adcam.pha.jhu.edu/~txitxo/ Normally interpolate 2 between adjacent templates

4. Flux Wavelength SED template fit

5. Redshift Probability prior: I = 26 without prior with prior Bayesian use of priors Benítez00 Output:

6. Benítez00 Redshift Inaccuracy (photo-z vs. spec-z) Poorness of Fit Poorest fits yield most accurate redshifts!

7.  2 = 4.27  2 = 0.11 Wavelength Flux  2 mod = 0.03  2 mod = 0.19

8. PHAT GOODS BPZ results (training set) Important to plot error bars and goodness-of-fit

9. PHAT GOODS BPZ results (training set) Single-peaked P(z) [ODDS  0.95] no error bars plotted

10. Most GOODS objects have good photometry ACS ground IRAC

11. …but some are bad ACS ground IRAC

12. ACS ground IRAC …some are ugly

13. Robust photo-z’s require Robust photometry One of the best methods (even if Peter doesn’t like it ;)

14. PSF-corrected aperture-matched photometry What is the best method?

15. PHOTEST  Photometry Testing  PSF Degradation vs. Model Fitting  Magnitude Uncertainties  Zeropoint Calibration  Object Detection & Deblending  …  Sounds like a job for a new group  Let’s meet in Greece 2009  Photometry Testing  PSF Degradation vs. Model Fitting  Magnitude Uncertainties  Zeropoint Calibration  Object Detection & Deblending  …  Sounds like a job for a new group  Let’s meet in Greece 2009

16. UDF NICMOS fluxes too low

17. NICMOS flux recalibration Objects w/ spec-z

18. Comprehensive Segmentation Map Forced into SExtractor

19. Wish List (Goals for PHAT?)  Improve SED library  more galaxy types  broader wavelength coverage  SED uncertainties  derived from population synthesis models??  Improve Priors  using UDF, surveys  Improve SED library  more galaxy types  broader wavelength coverage  SED uncertainties  derived from population synthesis models??  Improve Priors  using UDF, surveys

20. Optimal Filter Choice for a given amount of observing time Benítez et al. (2008) A&A submitted  4 - 5 filters is sub-optimal !  addition of near-IR helps somewhat  > 8 filters performs much better  4 - 5 filters is sub-optimal !  addition of near-IR helps somewhat  > 8 filters performs much better

21. Filters tested  = const   contiguousoverlapping

22. Photo-z completeness Best is > 8 overlapping filters Depth to which 80% of objects have ODDS ≥ 0.99

23. Photo-z accuracy for ODDS ≥ 0.99 objects Best is many non-overlapping (contiguous) filters

24. lab including CCD, atmosphere, mirror reflectivity ALHAMBRA Survey (Moles08) 20 medium-band (310Å wide) filters 3500 - 9700Å, supplemented by JHK s

25. ALHAMBRA Survey 1.5’ x 1.5’ 14-filter color image to cover 4+ sq deg

26.  8,000 - 10,000 sq deg  z < 0.9 - 1.0  4 - 5 years  6 sq deg camera  new 2-3m telescope to be built in Aragon, Spain  8,000 - 10,000 sq deg  z < 0.9 - 1.0  4 - 5 years  6 sq deg camera  new 2-3m telescope to be built in Aragon, Spain

27. PAU Survey : 40 100Å-wide filters (~4000-8000Å) + SDSS u & z

28. PAU Survey :  z/(1+z) L*, I < 23 LRGs

29. PAU Survey: BAO cosmological constraints

30. PAU Survey: relative w constraints