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ANNz: estimating photometric redshifts using artificial neural networks

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arxiv astro-ph/0311058 v2 pith:6PPBKOZ4 submitted 2003-11-03 astro-ph

ANNz: estimating photometric redshifts using artificial neural networks

Adrian A. Collister (Institute of Astronomy , Cambridge) , Ofer Lahav (University College London) This is my paper
classification astro-ph
keywords annzredshiftphotometricartificialavailabledatanetworksneural
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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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Cited by 6 Pith papers

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