Gamma-Ray Active Galactic Nucleus Type through Machine-Learning Algorithms

Understand

The Fermi Gamma-ray Space Telescope is producing the most detailed inventory of the gamma-ray sky to date.

• Despite tremendous achievements approximately 25% of all Fermi extragalactic sources in the Second Fermi LAT 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.