Realistic photometric redshifts

1. Realistic photometric redshifts Filipe Batoni Abdalla

2. Photometric Redshifts • Photometric redshifts (photo-z’s) are determined from the fluxes of galaxies through a set of filters • May be thought of as low-resolution spectroscopy • Photo-z signal comes primarily from strong galaxy spectral features, like the 4000 Å break, as they redshift through the filter bandpasses • All key projects depend crucially on photo-z’s • Photo-z calibrations will be • optimized using both simulated catalogs and images. Galaxy spectrum at 2 different redshifts, overlaid on griz and IR bandpasses

3. Hyper-z (Bolzonella et al. 2000) BPZ (Benitez 2000) Training Set Methods Template Fitting methods • Use a set of standard SED’s - templates (CWW80, etc.) • Calculate fluxes in filters of redshifted templates. • Match object’s fluxes (2 minimization) • Outputs type and redshift • Bayesian Photo-z • Determine functional relation • Examples Nearest Neighbors (Csabai et al. 2003) Polynomial Nearest Neighbors (Cunha et al. in prep. 2005) Polynomial (Connolly et al. 1995) Neural Network (Firth, Lahav & Somerville 2003; Collister & Lahav 2004) Cross correlations (Newman)

4. A case study: the DUNE satellite Catastrophic outliers Biases Uninformative region Photometric redshift biases: Abdalla et al. astro-ph:0705.1437

5. Degeneracies: u filter. • One major feature is the 4000 A break, without u filters there is no way of distinguishing a galaxy with a break at z= 0 and a galaxy with a flat SED

6. Degeneracies:Looking at the galaxy properties

7. Degeneracies: reddening

8. Degeneracies: catastrophic outliers

9. Degeneracies: Template correction

10. Degeneracies: incomplete training set

11. Av Type Surveys considered:

12. Signal to noise!!!!!!

13. Mock dependence: comparison to DES mocks. DES (grizY) DES+VISTA(JHK) M. Banerji, F. B. Abdalla, O. Lahav, H. Lin et al. In regions of interest photo-z are worst by 30%

14. Number of spectra needed

15. FOM: Results &Number of spectra needed • FOM prop 1/ dw x dw’ • IR improves error on DE parameters by a factor of 1.3-1.7 depending on optical data available • If u band data is available improvement is minimal • Number of spectra needed to calibrate these photo-z for wl is around 10^5 in each of the 5 redshift bins • Fisher matrix analysis marginalizing over errors in photo-z.

16. Cleaned photometric redshifts: Method: Motivation: Remove systematic effects associated to catastrophic outliers Calibrating these photo-z requires around a million spectra. Abdalla, Amara, Capak, Cypriano, Lahav, Rhodes 07

17. Effect on the dark energy measurements: • Can clean a catalogue without degrading dark energy measurements • In a cleaned catalogue systematic effects such as intrinsic alignments will be smaller • An error of dw x dw’=1/160 can be achieved

18. Error estimators in neural networks • Error seems to be OK for most cases but there are definitely problems with the error estimator • Furthermore, the training of a network does not use these errors for estimation optimal photo-z. i.e. noisy galaxies are weighted in the same way as well measured galaxies • Some error estimators are biased depending on the data quality.

19. Looking at techniques in real data:The Megaz-LRG catalogue. • 2SLAQ galaxies selected from the SDSS survey. Mainly red galaxies at redshift ranging from 0.4 to 0.7. • Even though photo-z are good for LRG given large 4000A break different techniques give different accuracies • Template fitting are better where there is less data • Training techniques are better where there is good training data. • Big case to develop a hybrid technique using proper error estimators. Abdalla et al (in prep.)

20. Comparison between different methods

21. N(z) for spec vs phot

23. Linking to Cosmic Shear & IA!!!!

24. Removing intrinsic alignments: • Finding a weighting function insensitive of shape-shear correlations. (Schneider/ Joachimi) - Is all the information still there? • Modelling of the intrinsic effects (Bridle & King.) - FOM definitely will decreased as need to constrain other parameters in GI correlations. • Using galaxy-shear correlation function. • Use of the 3-point correlation function to constrain the GI contributions (E. Semboloni.)

25. Are photo-zs good enough? • The FOM is a slow function of the photo-z quality if we consider only the shear-shear term. • If we consider modelling the shape-shear correlations this is not the case anymore. • This does not include the galaxy-shear correlation function so “reality” is most likely in between this “pessimistic” result and the optimistic result of neglecting GI Abdalla, Amara, Capak Cypriano, Lahav & Rhodes Bridle & King

26. ? Question: • Effect on model intrinsic alignement • Effect on weights (incorrect weight assigned) • Effect on 3-point correlation function

27. Conclusions • Photo-z can be very messy!!! • Degeneracy: lack of bands, reddening, 4000/ Lyman breaks, templates, incomplete training sets… • Different techniques give different answers, but hopefully a hybrid technique is possible • Error estimators can help but can be biased depending on the data • Links to Cosmic shear and IA : - How do the different methods to remove IA relate to photo-z requirements including catastrophic outliers and small biases