A COMPARISON OF PHOTO-Z CODES ON THE 2SLAQ LRG SAMPLE Manda 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 & 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< 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< 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) 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 Hybrid Use of a training set with spectroscopic redshifts to adjust templates e.g. ZEBRA and SDSS Template code.

PHOTO-Z ESTIMATORS (II) CODEMETHODREFERENCE HyperZTemplateBolzonella et al. (2000) BPZBayesianBenitez (2000) ANNzTrainingCollister & Lahav (2004) ImpZLiteTemplateBabbedge et al. (2004) SDSS TemplateHybridPadmanabhan et al. (2005) ZEBRAHybrid, BayesianFeldmann et al. (2006) LePhareTemplateIlbert et al. (2006)

OPTIMAL CONFIGURATIONS CODETEMPLATESTRAINING & PRIOR HyperZ4 x CWWNo Priors HyperZ8 x Bruzual & CharlotNo Priors BPZ17 x interpolated CWWFlat prior on L ANNzNoneTraining & validation sets ImpZLite7 x empirical templatesNo Priors SDSSOptimised evolving BC burst Training set ZEBRAOptimised 3 x CWWTraining set, self- consistent prior LePhare8 x PoggiantiNo Priors

STATISTICS 1 scatter around spec-z Bias In each photometric redshift bin: Repeat statistics in each spectroscopic redshift bin - 1 scatter around photo-z, bias and 1 scatter around mean photo-z. 1 scatter 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 (II)

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) 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 (II) Codef0f1f2f3f4 Hyp CWW24%32%35%7%1.5% Hyp BC26%32% 7%1.5% BPZ24%36%32%6%1% ANNz37%28%31%4%0.02% ZEBRA26%34% 5%0.8% LePhare26%34% 4%0.5% SDSS27%31%37%4.5%0.8%

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!