Open AccessEstimating photometric redshifts with artificial neural networks
Author(s) -
Firth Andrew E.,
Lahav Ofer,
Somerville Rachel S.
Publication year - 2003
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2003.06271.x
Subject(s) - redshift , physics , galaxy , photometric redshift , astrophysics , sky , artificial neural network , prior probability , brightness , redshift survey , angular diameter , bayesian probability , astronomy , artificial intelligence , stars , computer science
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 σ z ∼ 0.021 .