A COMPARISON OF PHOTO-Z CODES ON THE 2SLAQ LRG

SAMPLE

A COMPARISON OF PHOTO-Z CODES ON THE 2SLAQ LRG

SAMPLEManda Banerji (UCL)

Filipe Abdalla (UCL), Ofer Lahav (UCL), Valery Rashkov (Princeton)

Manda Banerji (UCL)

Filipe Abdalla (UCL), Ofer Lahav (UCL), Valery Rashkov (Princeton)

DATA - 2SLAQ & MegaZLRGDATA - 2SLAQ & MegaZLRG 2SLAQ - 2dF and SDSS LRG and QSO Survey

(Cannon et al., 2006) Spectroscopy of ~13,000 Luminous Red Galaxies

(LRGs) in the redshift range 0.3<z<0.8 - 5482 of these are used in this comparison.

Photometric redshift catalogue constructed from SDSS DR4 photometry using neural network code ANNz with 2SLAQ spectroscopic redshifts as a training set - MegaZLRG - 1,214,117 objects (Collister et al., 2007)

2SLAQ - 2dF and SDSS LRG and QSO Survey (Cannon et al., 2006)

Spectroscopy of ~13,000 Luminous Red Galaxies (LRGs) in the redshift range 0.3<z<0.8 - 5482 of these are used in this comparison.

Photometric redshift catalogue constructed from SDSS DR4 photometry using neural network code ANNz with 2SLAQ spectroscopic redshifts as a training set - MegaZLRG - 1,214,117 objects (Collister et al., 2007)

PHOTO-Z ESTIMATORS (I)PHOTO-Z ESTIMATORS (I)

Template Simple Template Fit - e.g.

HyperZ, ImpZ and LePhare Use of Bayesian priors -

e.g. BPZ and ZEBRA

Template Simple Template Fit - e.g.

HyperZ, ImpZ and LePhare Use of Bayesian priors -

e.g. BPZ and ZEBRA

Training Use of a training set with

spectroscopic redshifts to find empirical relation between redshift and colour. e.g. ANNz

Training Use of a training set with

spectroscopic redshifts to find empirical relation between redshift and colour. e.g. ANNz

Hybrid

Use of a training set with spectroscopic redshifts to adjust templates e.g. ZEBRA and

SDSS Template code.

PHOTO-Z ESTIMATORS (II)PHOTO-Z ESTIMATORS (II)

CODE METHOD REFERENCE

HyperZ Template Bolzonella et al. (2000)

BPZ Bayesian Benitez (2000)

ANNz Training Collister & Lahav (2004)

ImpZLite Template Babbedge et al. (2004)

SDSS Template Hybrid Padmanabhan et al. (2005)

ZEBRA Hybrid, Bayesian Feldmann et al. (2006)

LePhare Template Ilbert et al. (2006)

OPTIMAL CONFIGURATIONSOPTIMAL CONFIGURATIONSCODE TEMPLATES TRAINING & PRIOR

HyperZ 4 x CWW No Priors

HyperZ 8 x Bruzual & Charlot No Priors

BPZ 17 x interpolated CWW Flat prior on L

ANNz None Training & validation sets

ImpZLite 7 x empirical templates No Priors

SDSS Optimised evolving BC burst

Training set

ZEBRA Optimised 3 x CWW Training set, self-consistent prior

LePhare 8 x Poggianti No Priors

STATISTICSSTATISTICS

σ zphot = zspec − zphot( )2

12

bzphot = zspec −zphot

σ zphot 2 = zspec − zspec( )2

12

1σscatter around spec-z

Bias

In each photometric redshift bin:

Repeat statistics in each spectroscopic redshift bin - 1σscatter around photo-z, bias and 1σscatter around mean photo-z.

1σscatter around mean spec-z

STATISTICAL ERRORS IN PHOTO-Z => STATISTICAL ERRORS IN ESTIMATION OF COSMOLOGICAL PARAMETERS. EFFECT OF THESE ERRORS DEPENDS ON COSMOLOGICAL PROBE.

CODE COMPARISON (I) CODE COMPARISON (I)

CODE COMPARISON (II)CODE COMPARISON (II)

SUMMARY (I)SUMMARY (I) As expected, the availability of a complete and

representative training set means the empirical method, ANNz performs best in the intermediate redshift bins where there are plenty of spectroscopic redshifts.

LePhare performs very well particularly in the lower spectroscopic redshift bins suggesting the Poggianti templates may be a better fit to these galaxies than CWW.

ImpZ shows a large bias at high spectroscopic redshifts but if this is removed and the moment taken about the mean photo-z, ImpZ performs the best in the highest spec-z bins.

As expected, the availability of a complete and representative training set means the empirical method, ANNz performs best in the intermediate redshift bins where there are plenty of spectroscopic redshifts.

LePhare performs very well particularly in the lower spectroscopic redshift bins suggesting the Poggianti templates may be a better fit to these galaxies than CWW.

ImpZ shows a large bias at high spectroscopic redshifts but if this is removed and the moment taken about the mean photo-z, ImpZ performs the best in the highest spec-z bins.

SUMMARY (II)SUMMARY (II)

The HyperZ run with the Bruzual & Charlot templates gives better results than using the same code with CWW templates.

The SDSS template code gives very good results in the highest photo-z bins

ZEBRA, another template optimisation code shows a large bias as a function of photo-z but if this is removed and the moment taken about the mean spec-z in each photo-z bin, ZEBRA gives the best results at high photo-z.

The HyperZ run with the Bruzual & Charlot templates gives better results than using the same code with CWW templates.

The SDSS template code gives very good results in the highest photo-z bins

ZEBRA, another template optimisation code shows a large bias as a function of photo-z but if this is removed and the moment taken about the mean spec-z in each photo-z bin, ZEBRA gives the best results at high photo-z.

FRACTIONS STATISTIC (I)FRACTIONS STATISTIC (I)

FRACTIONS STATISTIC (II)FRACTIONS STATISTIC (II)

Code f0 f1 f2 f3 f4

Hyp CWW 24% 32% 35% 7% 1.5%

Hyp BC 26% 32% 32% 7% 1.5%

BPZ 24% 36% 32% 6% 1%

ANNz 37% 28% 31% 4% 0.02%

ZEBRA 26% 34% 34% 5% 0.8%

LePhare 26% 34% 34% 4% 0.5%

SDSS 27% 31% 37% 4.5% 0.8%

MegaZDR6 - Coming soon!MegaZDR6 - Coming soon! Depending on which statistic we are looking at

and in what redshift regime, different codes can be considered to produce the best results so which one do we use??!!

MegaZ DR6 is a catalogue of 1,543,596 photometric redshifts for Luminous Red Galaxies in SDSS DR6

Includes redshift estimates from all photo-z codes as well as error estimates from each of these

See Abdalla, Banerji, Lahav & Rashkov (2008, In prep) for more details!

Depending on which statistic we are looking at and in what redshift regime, different codes can be considered to produce the best results so which one do we use??!!

MegaZ DR6 is a catalogue of 1,543,596 photometric redshifts for Luminous Red Galaxies in SDSS DR6

Includes redshift estimates from all photo-z codes as well as error estimates from each of these

See Abdalla, Banerji, Lahav & Rashkov (2008, In prep) for more details!