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

TL;DR

This paper employs machine-learning algorithms to classify uncertain gamma-ray active galactic nuclei into specific subclasses, achieving high accuracy and aiding future observational efforts.

Contribution

It introduces the use of Random Forests and Support Vector Machines to classify AGN types based on gamma-ray spectral data, improving classification accuracy over previous methods.

Findings

85% accuracy in classifying AGN subclasses

High agreement with infrared-based predictions

Potential to guide follow-up spectroscopic observations

Abstract

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. 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 Lacs and flat-spectrum radio quasars with accuracy rates of 85%. Additionally, direct comparison of our results with class predictions made after following the infrared colour-colour space of Massaro et al. (2012) show that the agreement rate is over four-fifths for 54 overlapping sources, providing independent cross validation. These results can help tailor follow-up spectroscopic programs and inform future pointed surveys with ground-based Cherenkov telescopes.