ANNz: estimating photometric redshifts using artificial neural networks

Details ANNz: estimating photometric redshifts using artificial neural networks ANNz: estimating photometric redshifts using artificial neural networks

2003-11-03

arxiv.org/abs/astro-ph/0311058v2

Publ.Astron.Soc.Pac.116:345-351,2004

Astronomy and Astrophysics

Astrophysics

6 pages, 6 figures. Replaced to match version accepted by PASP (minor changes to original submission). The ANNz package may be

Scientific paper

10.1086/383254

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, and for this particular data set the r.m.s. redshift error in the range 0 < z < 0.7 is 0.023. Non-ideal 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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