Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning

TL;DR

This study uses Swift-XRT observations and machine learning models to identify and classify unassociated high-energy gamma-ray sources in the 3FHL catalog, primarily as BL Lac objects, enhancing source identification accuracy.

Contribution

It combines X-ray observations with machine learning classification to identify the nature of unassociated gamma-ray sources, specifically confirming many as BL Lac objects.

Findings

21 out of 26 X-ray sources are blazar-like, mainly BL Lacs.

Machine learning models predict all blazar candidates as BL Lacs.

Swift-XRT effectively identifies X-ray counterparts for unassociated gamma-ray sources.

Abstract

The Third Catalog of Hard Fermi Large Area Telescope Sources (3FHL) reports the detection of 1556 objects at E > 10 GeV. However, 177 sources remain unassociated and 23 are associated with a ROSAT X-ray detection of unknown origin. Pointed X-ray observations were conducted on 30 of these unassociated and unknown sources with Swift-XRT. A bright X-ray source counterpart was detected in 21 out of 30 fields. In five of these 21 fields, we detected more than one X-ray counterpart, totaling 26 X-ray sources analyzed. Multiwavelength data was compiled for each X-ray source detected. We find that 21 out of the 26 X-ray sources detected display the multiwavelength properties of blazars, while one X-ray source displays the characteristics of a Galactic source. Using trained decision tree, random forest, and support vector machine models, we predict all 21 blazar counterpart candidates to be BL Lacertae objects (BL Lacs). This is in agreement with BL Lacs being the most populous source class in the 3FHL.