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arXiv · 1507.01293

Using gamma regression for photometric redshifts of survey galaxies

J. Elliott·R. S. de Souza·A. Krone-Martins·E. Cameron·E. E. O. Ishida·J. Hilbe
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Abstract

Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of tools not commonly applied within astronomy, despite being widely used in other professions. With this technique, we achieve catastrophic outlier rates of the order of ~1%, that can be achieved in a matter of seconds on large datasets of size ~1,000,000. To make these techniques easily accessible to the astronomical community, we developed a set of libraries and tools that are publicly available.

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J. Elliott, R. S. de Souza, A. Krone-Martins, E. Cameron, E. E. O. Ishida, J. Hilbe. 2015-07-05. Using gamma regression for photometric redshifts of survey galaxies. https://doi.org/10.1007/978-3-319-19330-4_13

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