Gamma-ray active galactic nucleus type through machine-learning algorithms

Abstract

The Fermi Gamma-ray Space Telescope (Fermi) is producing the most detailed inventory of the gamma-ray sky to date. Despite tremendous achievements approximately 25 per cent of all Fermi extragalactic sources in the Second Fermi Large Area Telescope Catalogue (2FGL) are listed as active galactic nuclei (AGN) of uncertain type. Typically, these are suspected blazar candidates without a conclusive optical spectrum or lacking spectroscopic observations. Here, we explore the use of machine-learning algorithms – random forests and support vector machines – to predict specific AGN subclass based on observed gamma-ray spectral properties. After training and testing on identified/associated AGN from the 2FGL we find that 235 out of 269 AGN of uncertain type have properties compatible with gamma-ray BL Lacertae and flat-spectrum radio quasars with accuracy rates of 85 per cent. Additionally, direct comparison of our results with class predictions made after following the infrared colour–colour space of Massaro et al. shows that the agreement rate is over four-fifths for 54 overlapping sources, providing independent cross-validation. These results can help tailor follow-up spectroscopic programmes and inform future pointed surveys with ground-based Cherenkov telescopes.