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Oxford University Press, Monthly Notices of the Royal Astronomical Society, 4(339), p. 1195-1202, 2003

DOI: 10.1046/j.1365-8711.2003.06271.x

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Estimating photometric redshifts with artificial neural networks

Journal article published in 2003 by Andrew E. Firth ORCID, Ofer Lahav, Rachel S. Somerville
This paper is available in a repository.
This paper is available in a repository.

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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 ???20???000 galaxies from the Sloan Digital Sky Survey. The rms redshift error in the range z ??? 0.35 is ?? z ??? 0.021 .