1. Realistic photometric redshifts Filipe Batoni Abdalla

2. Galaxy spectrum at 2 different redshifts, overlaid on griz and IR bandpasses Photometric redshifts (photo-zs) 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-zs Photo-z calibrations will be optimized using both simulated catalogs and images. Photometric Redshifts

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

4. A case study: the DUNE satellite A case study: the DUNE satellite Photometric redshift biases: Catastrophic outliers Uninformative region 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 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. Surveys considered: AvType

12. Signal to noise!!!!!!

13. Mock dependence: comparison to DES mocks. DES+VISTA(JHK) DES (grizY) 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/ w x w FOM prop 1/ w x w IR improves error on DE parameters by a factor of 1.3-1.7 depending on optical data available 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 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 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. Fisher matrix analysis marginalizing over errors in photo-z.

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

17. Effect on the dark energy measurements: Can clean a catalogue without degrading 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 In a cleaned catalogue systematic effects such as intrinsic alignments will be smaller An error of An error of w x w=1/160 can be achieved w x w=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 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 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. 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. 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 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 Template fitting are better where there is less data Training techniques are better where there is good training data. Training techniques are better where there is good training data. Big case to develop a hybrid technique using proper error estimators. 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) Finding a weighting function insensitive of shape-shear correlations. (Schneider/ Joachimi) - Is all the information still there? - Is all the information still there? Modelling of the intrinsic effects (Bridle & King.) Modelling of the intrinsic effects (Bridle & King.) - FOM definitely will decreased as need to constrain other parameters in GI correlations. - FOM definitely will decreased as need to constrain other parameters in GI correlations. Using galaxy-shear correlation function. Using galaxy-shear correlation function. Use of the 3-point correlation function to constrain the GI contributions (E. Semboloni.) Use of the 3-point correlation function to constrain the GI contributions (E. Semboloni.)

25. Bridle & King Abdalla, Amara, Capak Cypriano, Lahav & Rhodes 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

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

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