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Title: Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning

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.
Authors:
 [1] ;  [2]
  1. Fakult├Ąt Physik, Technische Universit├Ąt Dortmund, D-44221 Dortmund (Germany)
  2. Department of Physics and Astronomy, Barnard College, Columbia University, NY 10027 (United States)
Publication Date:
OSTI Identifier:
22348033
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 782; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; CLASSIFICATION; GALAXIES; GALAXY NUCLEI; GAMMA RADIATION; GAMMA SOURCES; PERFORMANCE