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

TL;DR

This study uses spectral analysis and neural networks on multiwavelength data to classify unassociated Fermi gamma-ray sources as pulsars or blazars, improving classification accuracy and expanding catalogs.

Contribution

It introduces a neural network approach leveraging gamma-ray, X-ray, and UV/optical spectral data for classifying unassociated sources, with higher accuracy than previous methods.

Findings

132 likely blazars identified with high confidence

14 likely pulsars identified with high confidence

Neural network classifier outperforms previous methods

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.