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

TL;DR

This study uses Swift X-ray observations and machine learning algorithms to classify unassociated Fermi gamma-ray sources as blazars or pulsars with high accuracy, improving source identification.

Contribution

It introduces a machine learning approach combining X-ray and gamma-ray data to distinguish between blazar and pulsar counterparts of unassociated gamma-ray sources.

Findings

97-99% classification accuracy with decision tree and random forest.

173 blazar candidates identified with >90% probability.

13 pulsar candidates with <10% blazar probability.

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}$ $\geq$ 90% for each of these sources, and 134 of these possible blazar source associations had P$_{bzr}$ $\geq$ 99%. The results yielded 13 sources with P$_{bzr}$ $\leq$ 10%, which we deemed as reasonable candidates for pulsars, 7 of which result with P$_{bzr}$ $\leq$ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.