2002/03/31 by Andrew E. Firth, Ofer Lahav, Rachel S. Somerville
Physics and Astronomy · #astro-ph
paper · pdf · doi:10.1046/j.1365-8711.2003.06271.x
published as Mon.Not.Roy.Astron.Soc. 339 (2003) 1195 · Submitted to MNRAS, 9 pages, 9 figures, substantial improvements to paper structure
arxiv created 2002/10/21 · arxiv updated 2009/12/01
A new approach to estimating photometric redshifts - using Artificial Neural Networks (ANNs) - is investigated. Unlike the standard template-fitting photometric redshift technique, a large spectroscopically-identified training set is required but, where one is available, ANNs produce photometric redshift accuracies at least as good as and often better than the template-fitting method. The Bayesian priors on the underlying redshift distribution are automatically taken into account. Furthermore, inputs other than galaxy colours - such as morphology, angular size and surface brightness - may be easily incorporated, and their utility assessed. Different ANN architectures are tested on a semi-analytic model galaxy catalogue and the results are compared with the template-fitting method. Finally the method is tested on a sample of ~ 20000 galaxies from the Sloan Digital Sky Survey. The r.m.s. redshift error in the range z < 0.35 is ~ 0.021.