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

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 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.

arXiv comment13 pages text, 6 figures, 5 tables including 2 catalog tables

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Cited by in corpus (14)

• Data mining techniques on astronomical spectra data. II : Classification Analysis
• Using Neural Networks to Differentiate Newly Discovered BL Lacs and FSRQs among the 4FGL Unassociated Sources Employing Gamma-ray, X-ray, UV/Optical and IR Data
• Classification of blazar candidates of unknown type in Fermi 4LAC by unanimous voting from multiple Machine Learning Algorithms
• Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
• A new redback pulsar candidate 4FGL J2054.2+6904
• Identifying the 3FHL Catalog. VI. Swift Observations of 3FHL Unassociated Objects with Source Classification via Machine Learning
• A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources
• Non-thermal GeV emission from the Nereides nebula: confirming the nature of the supernova remnant G107.7-5.1
• Nature of 4FGL J1838.2+3223: a flaring `spider' pulsar candidate
• Testing the Blazar Sequence with Spectra of Recently Discovered Dim Blazars from the Fermi Unassociated Catalog
• Search for the multiwavelength counterparts to extragalactic unassociated Fermi γ-ray sources
• Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network
• Classification of Fermi-LAT blazars with Bayesian neural networks
• Exploring the nature of Galactic unassociated sources detected by the Fermi-LAT