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Title: 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.

Authors:
 [1]
  1. Fakultät Physik, Technische Universität Dortmund, D-44221 Dortmund (Germany)
Publication Date:
OSTI Identifier:
22348033
Resource Type:
Journal Article
Journal Name:
Astrophysical Journal
Additional Journal Information:
Journal Volume: 782; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0004-637X
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ALGORITHMS; CLASSIFICATION; GALAXIES; GALAXY NUCLEI; GAMMA RADIATION; GAMMA SOURCES; PERFORMANCE

Citation Formats

Doert, M., and Errando, M., E-mail: marlene.doert@tu-dortmund.de, E-mail: errando@astro.columbia.edu. Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning. United States: N. p., 2014. Web. doi:10.1088/0004-637X/782/1/41.
Doert, M., & Errando, M., E-mail: marlene.doert@tu-dortmund.de, E-mail: errando@astro.columbia.edu. Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning. United States. https://doi.org/10.1088/0004-637X/782/1/41
Doert, M., and Errando, M., E-mail: marlene.doert@tu-dortmund.de, E-mail: errando@astro.columbia.edu. 2014. "Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning". United States. https://doi.org/10.1088/0004-637X/782/1/41.
@article{osti_22348033,
title = {Search for gamma-ray-emitting active galactic nuclei in the Fermi-LAT unassociated sample using machine learning},
author = {Doert, M. and Errando, M., E-mail: marlene.doert@tu-dortmund.de, E-mail: errando@astro.columbia.edu},
abstractNote = {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.},
doi = {10.1088/0004-637X/782/1/41},
url = {https://www.osti.gov/biblio/22348033}, journal = {Astrophysical Journal},
issn = {0004-637X},
number = 1,
volume = 782,
place = {United States},
year = {Mon Feb 10 00:00:00 EST 2014},
month = {Mon Feb 10 00:00:00 EST 2014}
}