ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks | Zendy

Adrian Collister | Zendy; O. Lahav | Zendy

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ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks

Author(s) -

Adrian Collister,

O. Lahav

Publication year - 2004

Publication title -

publications of the astronomical society of the pacific

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.294

H-Index - 172

eISSN - 1538-3873

pISSN - 0004-6280

DOI - 10.1086/383254

Subject(s) - redshift , photometric redshift , photometry (optics) , galaxy , sky , artificial neural network , astrophysics , physics , set (abstract data type) , computer science , artificial intelligence , stars , programming language

We introduce ANNz, a freely available software package for photometricredshift estimation using Artificial Neural Networks. ANNz learns the relationbetween photometry and redshift from an appropriate training set of galaxiesfor which the redshift is already known. Where a large and representativetraining set is available ANNz is a highly competitive tool when compared withtraditional template-fitting methods. The ANNz package is demonstrated on the Sloan Digital Sky Survey Data Release1, and for this particular data set the r.m.s. redshift error in the range 0

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