Classification of New X-ray Counterparts for Fermi Unassociated Gamma Ray Sources Using the Swift X-Ray Telescope

Abstract

Approximately one-third of the gamma-ray sources in the third Fermi-LAT catalog are unidentified or unassociated with objects at other wavelengths. Observations with Swift-XRT have yielded possible counterparts in $\sim$30% of these source regions. The objective of this work is to identify the nature of these possible counterparts, utilizing their gamma ray properties coupled with the Swift derived X-ray properties. The majority of the known sources in the Fermi catalogs are blazars, which constitute the bulk of the extragalactic gamma-ray source population. The galactic population on the other hand is dominated by pulsars. Blazars and pulsars occupy different parameter space when X-ray fluxes are compared with various gamma-ray properties. In this work, we utilize the X-ray observations performed with the Swift-XRT for the unknown Fermi sources and compare their X-ray and gamma-ray properties to differentiate between the two source classes. We employ two machine learning algorithms, decision tree and random forest classifier, to our high signal-to-noise ratio sample of 217 sources, each of which correspond to Fermi unassociated regions. The accuracy score for both methods were found to be 97% and 99%, respectively. The random forest classifier, which is based on the application of a multitude of decision trees, associated a probability value (P${}_{bzr}$) for each source to be a blazar. This yielded 173 blazar candidates with P${}_{bzr}$ $\ge$ 90% for each of these sources, and 134 of these possible blazar source associations had P${}_{bzr}$ $\ge$ 99%. The results yielded 13 sources with P${}_{bzr}$ $\le$ 10%, which we deemed as reasonable candidates for pulsars, 7 of which result with P${}_{bzr}$ $\le$ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.

accepted in ApJ

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• X-ray Spectra and Multiwavelength Machine Learning Classification for Likely Counterparts to Fermi 3FGL Unassociated Sources
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms
• 4FGL J1120.0-2204: A Unique Gamma-ray Bright Neutron Star Binary with an Extremely Low Mass Proto-White Dwarf
• Classifying blazar candidates from the 3FGL unassociated catalog into BL Lacs and FSRQs using Swift and WISE data
• Search for the multiwavelength counterparts to extragalactic unassociated Fermi γ-ray sources
• Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network