2013/12/19 by M. Doert, M. Errando · 1 citation
Physics and Astronomy · #Active galactic nucleus #Astrophysics and Cosmic Phenomena #Characterization (materials science) #Galaxies: Formation, Evolution, Phenomena #Gamma-ray bursts and supernovae #Pattern recognition (psychology) #Population #Robustness (evolution) #Sample (material) #astro-ph.HE #astro-ph.IM #physics.data-an
paper · pdf · doi:10.1088/0004-637x/782/1/41
published as The Astrophysical Journal, 782:41 (7pp), 2014 February 10 · 7 pages, 6 figures, 3 tables; accepted for publication in ApJ. A full version of Table 3 in ASCII format is available as ancillary file
arxiv created 2013/12/19 · openalex publication_date 2014/01/24 · arxiv updated 2014/01/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.