A data mining approach to recognizing source classes for unassociated gamma-ray sources

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The Fermi-LAT 3rd source catalog (3FGL) provides the gamma-ray properties for 3034 gamma- ray sources. While 2024 sources in the 3FGL are associated with AGNs (58 % of the total), pulsars (5 %) and the other classes (4 %), 1010 sources (33 %) remain as unassociated sources. In recognizing source classes for unassociated gamma-ray sources of the Fermi-LAT source cat- alogs, various data mining techniques have been applied, e.g. classification tree and artificial neural network. As a robust alternative to these data mining techniques, we present the Maha- lanobis Taguchi (MT) method to recognize source classes. The MT method creates a multidimen- sional unit space from characteristic variables of a normal class (e.g. AGN) to identify sources of the normal class from those of the other classes using Mahalanobis distances. In this paper, we present the results of the source classification for the unassociated gamma-ray sources in 3FGL by applying the MT method. We also discuss a possibility of dark matter Galactic subhalos for the unclassified sources at jbj > 20°.

Original languageEnglish
Article number857
JournalProc. of 34th ICRC
Publication statusPublished - 2015


ASJC Scopus subject areas

  • General

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