Using Gamma Regression for Photometric Redshifts of Survey Galaxies

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.