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.
CITATION STYLE
Elliott, J., De Souza, R. S., Krone-Martins, A., Cameron, E., Ishida, E. E. O., & Hilbe, J. (2016). Using gamma regression for photometric redshifts of survey galaxies. In Astrophysics and Space Science Proceedings (Vol. 42, pp. 91–96). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-19330-4_13
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