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Gamma-ray Source Classification Via Machine Learning

Presentation #106.33 in the session “AGN (Poster)”.

Published onApr 01, 2022
Gamma-ray Source Classification Via Machine Learning

The Fermi Large Area Telescope (Fermi-LAT) 3FHL catalog provides an opportunity to identify unassociated and unclassified blazar candidates via X-ray counterparts. Doing so enables future optical follow up that will confirm the candidates’ redshift which will provide source constraints for the upcoming Cerenkov Telescope Array (CTA). Utilizing the Swift satellite, we have identified a likely X-ray counterpart of 21 unassociated 3FHL sources. After extracting the flux and spectral index, our machine learning algorithm classified the sources as blazars of the BL Lacertae type. In this talk, we present the results from the analysis and discuss the implications on this population of unassociated sources.

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