Gamma-ray AGN type determination

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.