Realistic photometric Realistic photometric redshiftsredshifts

Filipe Batoni Abdalla

Galaxy spectrum at 2 different redshifts, overlaid on griz and IR bandpasses

• 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.

Photometric Redshifts Photometric Redshifts

Training Set Training Set MethodsMethods• Determine Determine

functional relationfunctional relation

zphot zphot (m,c)

• ExamplesExamples

Neural Network(Firth, Lahav & Somerville 2003; Collister & Lahav 2004)

Polynomial Nearest Neighbors(Cunha et al. in prep. 2005)

Template Template Fitting Fitting methodsmethods

• Use a set of standard SED’s - Use a set of standard SED’s - templates (CWW80, etc.)templates (CWW80, etc.)

• Calculate fluxes in filters of Calculate fluxes in filters of redshifted templates.redshifted templates.

• Match object’s fluxes (Match object’s fluxes (22 minimization)minimization)

• Outputs type and redshiftOutputs type and redshift

• Bayesian Photo-zBayesian 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)Cross correlations (Newman)

• A case study: the DUNE satelliteA case study: the DUNE satellite

Photometric redshift Photometric redshift biases:biases:

Catastrophicoutliers

Uninformativeregion

Biases

Abdalla et al. astro-ph:0705.1437

Degeneracies: u filter.Degeneracies: u filter.• One major feature is the One major feature is the

4000 A break, without u 4000 A break, without u filters there is no way of filters there is no way of distinguishing a galaxy with distinguishing a galaxy with a break at z= 0 and a a break at z= 0 and a

galaxy with a flat SEDgalaxy with a flat SED

Degeneracies:Degeneracies:Looking at the galaxy Looking at the galaxy propertiesproperties

Degeneracies: reddeningDegeneracies: reddening

Degeneracies: catastrophic Degeneracies: catastrophic outliersoutliers

Degeneracies: Template Degeneracies: Template correctioncorrection

Degeneracies: Degeneracies: incomplete incomplete training settraining set

Surveys considered:Surveys considered:

Av Type

Signal to Signal to noise!!!!!!noise!!!!!!

Mock dependence: Mock dependence: comparison to DES mocks.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%

Number of Number of spectra neededspectra needed

FOM: Results &FOM: Results &Number of spectra Number of spectra neededneeded• FOM prop 1/ FOM prop 1/ w x w x w’w’

• IR improves error on DE IR improves error on DE parameters by a factor parameters by a factor of 1.3-1.7 depending of 1.3-1.7 depending on optical data on optical data availableavailable

• If u band data is If u band data is available improvement available improvement is minimalis minimal

• Number of spectra Number of spectra needed to calibrate needed to calibrate these photo-z for wl is these photo-z for wl is around 10^5 in each of around 10^5 in each of the 5 redshift binsthe 5 redshift bins

• Fisher matrix analysis Fisher matrix analysis marginalizing over marginalizing over errors in photo-z.errors in photo-z.

Cleaned photometric Cleaned photometric redshifts:redshifts:

Motivation:Remove systematiceffects associated to catastrophic outliers

Calibrating these photo-z requires around a million spectra.

Method:

Abdalla, Amara, Capak,Cypriano, Lahav, Rhodes 07

Effect on the dark energy Effect on the dark energy measurements:measurements:

• Can clean a catalogue Can clean a catalogue without degrading dark without degrading dark energy measurementsenergy measurements

• In a cleaned catalogue In a cleaned catalogue systematic effects such systematic effects such as intrinsic alignments as intrinsic alignments will be smallerwill be smaller

• An error of An error of w x w x w’=1/160 can w’=1/160 can

be achievedbe achieved

Error estimators in neural Error estimators in neural networksnetworks

• Error seems to be OK Error seems to be OK for most cases but for most cases but there are definitely there are definitely problems with the problems with the error estimatorerror estimator

• Furthermore, the Furthermore, the training of a network training of a network does not use these does not use these errors for estimation errors for estimation optimal photo-z. i.e. optimal photo-z. i.e. noisy galaxies are noisy galaxies are weighted in the weighted in the same way as well same way as well measured galaxiesmeasured galaxies

• Some error Some error estimators are estimators are biased depending on biased depending on the data quality.the data quality.

Looking at techniques in real Looking at techniques in real data:data:The Megaz-LRG catalogue.The Megaz-LRG catalogue.

• 2SLAQ galaxies selected 2SLAQ galaxies selected from the SDSS survey. from the SDSS survey. Mainly red galaxies at Mainly red galaxies at redshift ranging from 0.4 redshift ranging from 0.4 to 0.7.to 0.7.

• Even though photo-z are Even though photo-z are good for LRG given large good for LRG given large 4000A break different 4000A break different techniques give different techniques give different accuraciesaccuracies

• Template fitting are better Template fitting are better where there is less datawhere there is less data

• Training techniques are Training techniques are better where there is good better where there is good training data.training data.

• Big case to develop a Big case to develop a hybrid technique using hybrid technique using proper error estimators.proper error estimators.

Abdalla et al (in prep.)

Comparison between different Comparison between different methodsmethods

N(z) for spec vs photN(z) for spec vs phot

Linking to Cosmic Shear & Linking to Cosmic Shear & IA!!!!IA!!!!

Removing intrinsic Removing intrinsic alignments:alignments:• Finding a weighting function insensitive of Finding a weighting function insensitive of

shape-shear correlations. (Schneider/ Joachimi)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 - FOM definitely will decreased as need to constrain other parameters in GI correlations.constrain other parameters in GI correlations.

• Using galaxy-shear correlation function.Using galaxy-shear correlation function.

• Use of the 3-point correlation function to Use of the 3-point correlation function to constrain the GI contributions (E. Semboloni.)constrain the GI contributions (E. Semboloni.)

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Are photo-zs good Are photo-zs good enough?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

Question:Question:

• Effect on model intrinsic Effect on model intrinsic alignementalignement

• Effect on weights Effect on weights (incorrect weight (incorrect weight assigned)assigned)

• Effect on 3-point Effect on 3-point correlation functioncorrelation function

?

ConclusionsConclusions

• Photo-z can be very messy!!!Photo-z can be very messy!!!• Degeneracy: lack of bands, reddening, 4000/ Degeneracy: lack of bands, reddening, 4000/

Lyman breaks, templates, incomplete training Lyman breaks, templates, incomplete training sets…sets…

• Different techniques give different answers, but Different techniques give different answers, but hopefully a hybrid technique is possiblehopefully a hybrid technique is possible

• Error estimators can help but can be biased Error estimators can help but can be biased depending on the datadepending on the data

• Links to Cosmic shear and IA :Links to Cosmic shear and IA :- How do the different methods to remove IA - How do the different methods to remove IA relate to photo-z requirements including relate to photo-z requirements including catastrophic outliers and small biases catastrophic outliers and small biases