Machine-learning classifiers for Fermi AGN

Author/Creator ORCID

Date

2012-12-11

Department

Program

Citation of Original Publication

T. Hassan, N. Mirabal, I. Oya, and J. L. Contreras, Machine-learning classifiers for Fermi AGN, AIP Conference Proceedings 1505, 701 (2012); https://doi.org/10.1063/1.4772356

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
© 2012 AIP Publishing LLC

Subjects

Abstract

The Fermi Gamma-ray Space Telescope is generating the most detailed map of the gamma-ray sky. While tremendously successful, approximately 25% of all associated Fermi extragalactic sources in the Second Fermi LAT Catalog (2FGL) are listed as active galactic nuclei (AGN) of uncertain type. Most of 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 properties.