Wuppertal 2015 – scientific programme
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T: Fachverband Teilchenphysik
T 27: Gammaastronomie II
T 27.8: Talk
Monday, March 9, 2015, 18:30–18:45, I.13.71 (HS 28)
Search for gamma-ray emitting AGN among unidentified Fermi-LAT sources using machine learning algorithms — •Marlene Doert1,2, Sabrina Einecke1, and Manel Errando3 — 1Technische Universität Dortmund, Germany — 2Ruhr-Universität Bochum, Germany — 3Barnard College, Columbia University, New York City, USA
The second Fermi-LAT source catalog (2FGL) is the deepest all-sky survey of the gamma-ray sky currently available to the community. Out of the 1873 catalog sources, 576 remain unassociated. We present a search for active galactic nuclei (AGN) among these unassociated objects, which aims at a reduction of the number of unassociated gamma-ray sources and a more complete characterization of the population of gamma-ray emitting AGN. Our study uses two complimentary machine learning algorithms which are individually trained on the gamma-ray properties of associated 2FGL sources and thereafter applied to the unassociated sample. The intersection of the two methods yields a high-confidence sample of 231 AGN candidate sources. We estimate the performance of the classification by taking inherent differences between the samples of associated and unassociated 2FGL sources into account. A search for infra-red counterparts and first results from follow-up studies in the X-ray band using Swift satellite data for a subset of our AGN candidates are also presented.