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Paper: Implementing Probabilistic Photometric Redshifts
Volume: 475, Astronomical Data Analysis Software and Systems XXII
Page: 69
Authors: Carrasco Kind, M.; Brunner, R. J.
Abstract: Photometric redshifts have become more important with the growth of large imaging surveys. But their basic implementation has not changed significantly from their original development, as most techniques provide a single estimate and a computed error for the source redshift. In this paper, we present a new approach that provides accurate probability density functions (PDF) of redshifts for galaxies taken from the DEEP2 survey by efficiently combining standard template fitting techniques with powerful machine learning methods in a new, fully probabilistic manner.
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