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Estimating photometric redshifts with artificial neural networks

Andrew E. Firth·Ofer Lahav·Rachel S. Somerville
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Abstract

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.

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Andrew E. Firth, Ofer Lahav, Rachel S. Somerville. 2002-10-21. Estimating photometric redshifts with artificial neural networks. https://doi.org/10.1046/j.1365-8711.2003.06271.x

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