Fermi Gamma-ray Space Telescope

Automated classification of X-ray sources in the fields of GeV and TeV sources

J. Hare
B. Rangelov, I. Volkov, O. Kargaltsev, G. Pavlov

Abstract:

New generation gamma-ray observatories (such as Fermi-LAT, H.E.S.S., and VERITAS) have discovered hundreds of Galactic sources, many of which are unidentified and some are extended. Multiwavelength observations are often the only way to constrain the nature of these sources. We have been classifying X-ray sources residing within the extent of the unidentified gamma-ray sources using a machine-learning approach. By compiling mutiwavelength information (X-ray, optical, infrared, radio) from existing catalogs, a training data set has been created and used to classify the unidentified sources with several machine-learning algorithms. We will present the methods and the results of our classifications of X-ray sources in the fields of GeV and TeV sources observed with Chandra and XMM-Newton X-ray observatories, including the cases with diffuse X-ray emission.