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.
Comment: Refereed Proceeding of "The Universe of Digital Sky Surveys" conference held at the INAF - Observatory of Capodimonte, Naples, on 25th-28th November 2014, to be published in the Astrophysics and Space Science Proceedings, edited by Longo, Napolitano, Marconi, Paolillo, Iodice, 6 pages, and 1 figure