A Bayesian Classifier for Photometric Redshifts: Identification of high redshift clusters

Abstract

Photometric redshift classifiers provide a means of estimating galaxy redshifts from observations using a small number of broad-band filters. However, the accuracy with which redshifts can be determined is sensitive to the star formation history of the galaxy, for example the effects of age, metallicity and on-going star formation. We present a photometric classifier that explicitly takes into account the degeneracies implied by these variations, based on the flexible stellar population synthesis code of Kodama & Arimoto. The situation is encouraging since many of the variations in stellar populations introduce colour changes that are degenerate. We use a Bayesian inversion scheme to estimate the likely range of redshifts compatible with the observed colours. When applied to existing multi-band photometry for Abell 370, most of the cluster members are correctly recovered with little field contamination. The inverter is focussed on the recovery of a wide variety of galaxy populations in distant (z~1) clusters from broad band colours covering the 4000 angstrom break. It is found that this can be achieved with impressive accuracy ($|\Delta z|<0.1$), allowing detailed investigation into the evolution of cluster galaxies with little selection bias.

18 pages, including 15 figures, Accepted for publication in MNRAS

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