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Realistic photometric redshifts. Filipe Batoni Abdalla. 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

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Realistic photometric redshifts

Filipe Batoni Abdalla


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

training set methods

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)


(Connolly et al. 1995)

Neural Network

(Firth, Lahav & Somerville 2003;

Collister & Lahav 2004)

Cross correlations (Newman)

photometric redshift biases
A case study: the DUNE satellite






Photometric redshift biases:

Abdalla et al. astro-ph:0705.1437

degeneracies u filter
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
mock dependence comparison to des mocks
Mock dependence: comparison to DES mocks.

DES (grizY)


M. Banerji, F. B. Abdalla, O. Lahav, H. Lin et al.

In regions of interest photo-z

are worst by 30%

fom results number of spectra needed
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.
cleaned photometric redshifts
Cleaned photometric redshifts:



Remove systematic

effects associated

to catastrophic outliers

Calibrating these

photo-z requires

around a million spectra.

Abdalla, Amara, Capak,

Cypriano, Lahav, Rhodes 07

effect on the dark energy measurements
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

error estimators in neural networks
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.
looking at techniques in real data the megaz lrg catalogue
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.)

removing intrinsic alignments
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.)
are photo zs good enough
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



  • Effect on model intrinsic alignement
  • Effect on weights (incorrect weight assigned)
  • Effect on 3-point correlation function
  • 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