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

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

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

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  1. A COMPARISON OF PHOTO-Z CODES ON THE 2SLAQ LRG SAMPLE Manda Banerji (UCL) Filipe Abdalla (UCL), Ofer Lahav (UCL), Valery Rashkov (Princeton)

  2. 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<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)

  3. 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 PHOTO-Z ESTIMATORS (I) • Hybrid • Use of a training set with spectroscopic redshifts to adjust templates e.g. ZEBRA and SDSS Template code.

  4. PHOTO-Z ESTIMATORS (II)

  5. OPTIMAL CONFIGURATIONS

  6. STATISTICS In each photometric redshift bin: STATISTICAL ERRORS IN PHOTO-Z => STATISTICAL ERRORS IN ESTIMATION OF COSMOLOGICAL PARAMETERS. EFFECT OF THESE ERRORS DEPENDS ON COSMOLOGICAL PROBE. 1scatter around spec-z Bias 1scatter around mean spec-z Repeat statistics in each spectroscopic redshift bin - 1scatter around photo-z, bias and 1scatter around mean photo-z.

  7. CODE COMPARISON (I)

  8. CODE COMPARISON (II)

  9. 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.

  10. SUMMARY (II) • The HyperZ run with the Bruzual & Charlot templates gives better results than using the same code with CWW templates. • The SDSS templatecode 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.

  11. FRACTIONS STATISTIC (I)

  12. FRACTIONS STATISTIC (II)

  13. 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!

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