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Counterpart Detection, Spectral Analysis, ML Classification, and Followup of 4FGL Unassociated Sources

Presentation #106.05 in the session “AGN (Poster)”.

Published onApr 01, 2022
Counterpart Detection, Spectral Analysis, ML Classification, and Followup of 4FGL Unassociated Sources

We present results from observations and classification of Fermi-LAT unassociated sources, some of the most enigmatic gamma-ray sources in the sky. These sources have no confident association with a known object at lower energies, but our observations with the Niel Gehrels Swift Observatory have identified hundreds of likely X-ray/UV/optical counterparts in the uncertainty ellipses of the 4FGL unassociated sources. We have conducted spectral fitting of 205 possible X-ray/UV/optical counterparts to 4FGL unassociated targets found thus far, using a neural network to classify the spectra into likely pulsars and blazars. From our primary sample of 174 Fermi sources with a single X-ray/UV/optical counterpart, we found 132 likely blazars and 14 likely pulsars. These classifications expand previous catalogs of gamma-ray pulsars and blazars by including systematically dimmer sources. Our neural network classifier showed that low-energy parameters like UVOT magnitudes are important components for discriminating pulsars from blazars. Compared to previous classification approaches our neural network classifier achieves significantly higher validation accuracy, suggesting that multiwavelength analysis is a valuable tool for confident classification of Fermi unassociated sources. We also present results from ongoing follow-up projects, including new observations of newly discovered radio pulsars and investigations of multiwavelength emission from a low-mass X-ray binary.


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