Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources

Stephen Kerby, Amanpreet Kaur, Abraham D. Falcone, Ryan Eskenasy, Fredric Hancock, Michael C. Stroh, Elizabeth C. Ferrara, Paul S. Ray, Jamie A. Kennea, Eric Grove

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the uncertainty ellipses of the unassociated sources. In this work we present spectral fitting results for 205 possible X-ray/UV/optical counterparts to 4FGL unassociated targets. Assuming that the unassociated sources contain mostly pulsars and blazars, we develop a neural network classifier approach that applies gamma-ray, X-ray, and UV/optical spectral parameters to yield a descriptive classification of unassociated spectra into pulsars and blazars. From our primary sample of 174 Fermi sources with a single X-ray/UV/optical counterpart, we present 132 P bzr > 0.99 likely blazars and 14 P bzr < 0.01 likely pulsars, with 28 remaining ambiguous. These subsets of the unassociated sources suggest a systematic expansion to catalogs of gamma-ray pulsars and blazars. Compared to previous classification approaches our neural network classifier achieves significantly higher validation accuracy and returns more bifurcated P bzr values, suggesting that multiwavelength analysis is a valuable tool for confident classification of Fermi unassociated sources.

Original languageEnglish (US)
Article number75
JournalAstrophysical Journal
Volume923
Issue number1
DOIs
StatePublished - Dec 10 2021

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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