Machine-learning classifiers for Fermi AGN
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Author/Creator ORCID
Date
2012-12-11
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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
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© 2012 AIP Publishing LLC
© 2012 AIP Publishing LLC
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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.