Year: 2003
Type: article
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 σz∼ 0.021.
Authors: Andrew E. Firth, Ofer Lahav, Rachel S. Somerville
Institutions: University of Cambridge, University of Michigan
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2161966883