ANNz: estimating photometric redshifts using artificial neural networks

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  • 3 November 2003
Abstract
We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz 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 ANNz is a highly competitive tool when compared with traditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release 1. The r.m.s. redshift error in the range 0 < z < 0.7 is 0.024. Various extensions to the basic method are demonstrated using the Sloan Data. Finally, more realistic conditions (spectroscopic sets which are small, or which are brighter than the photometric set for which redshifts are required) are simulated and the impact on the photometric redshift accuracy assessed.

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