http://arxiv.org/abs/1808.02846
Photometric redshifts are necessary for enabling large-scale multicolour galaxy surveys to interpret their data and constrain cosmological parameters. While the increased depth of future surveys such as the Large Synoptic Survey Telescope (LSST) will produce higher precision constraints, it will also increase the fraction of sources that are blended. In this paper, we present a Bayesian photometric redshift method for blended sources with an arbitrary number of intrinsic components. This method generalises the template-based BPZ method of Benitez (2000), and produces joint posterior distributions for the component redshifts that allow uncertainties to be propagated in a principled way. Using Bayesian model comparison, we infer the probability that a source is blended and the number of components that it contains. We make available blendz, a Python implementation of our method.
D. Jones and A. Heavens
Thu, 9 Aug 18
21/57
Comments: 18 pages. Submitted to MNRAS. Python implementation available at this http URL
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