Search for Gamma-ray-emitting Active Galactic Nuclei in the Fermi-LAT Unassociated Sample Using Machine Learning

You are here: Home / Submitted Papers / 2014 / Search for Gamma-ray-emitting Active Galactic Nuclei in the Fermi-LAT Unassociated Sample Using Machine Learning

Abstract

The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey available in the gamma-ray band. It contains 1873 sources, of which 576 remain unassociated. Machine-learning algorithms can be trained on the gamma-ray properties of known active galactic nuclei (AGNs) to find objects with AGN-like properties in the unassociated sample. This analysis finds 231 high-confidence AGN candidates, with increased robustness provided by intersecting two complementary algorithms. A method to estimate the performance of the classification algorithm is also presented, that takes into account the differences between associated and unassociated gamma-ray sources. Follow-up observations targeting AGN candidates, or studies of multiwavelength archival data, will reduce the number of unassociated gamma-ray sources and contribute to a more complete characterization of the population of gamma-ray emitting AGNs.

Author

Doert, M.; Errando, M.

Journal

Astrophysical Journal

Paper Publication Date

February 2014

Paper Type

Astroinformatics