Year: 2004
Type: article
Abstract: We introduce ANN z , a freely available software package for photometric redshift estimation using artificial neural networks. ANN z learns the relation between photometry and redshift from an appropriate training set of galaxies for which the redshift is already known. Where a large and representative training set is available, ANN z is a highly competitive tool when compared with traditional template‐fitting methods. The ANN z package is demonstrated on the Sloan Digital Sky Survey Data Release 1, and for this particular data set the rms redshift error in the range 0≲z≲0.7 is σ rms = 0.023. Nonideal conditions (spectroscopic sets that are small or brighter than the photometric set for which redshifts are required) are simulated, and the impact on the photometric redshift accuracy is assessed. 2
DOI: 10.1086/383254
Authors: Adrian A. Collister, Ofer Lahav
Institution: University of Cambridge
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2152876174