X-ray Spectra and Multiwavelength Machine Learning Classification for Likely Counterparts to Fermi 3FGL Unassociated Sources

Abstract

We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL unassociated sources. Characterization and classification of these sources allows for more complete population studies of the high-energy sky. Most of these X-ray spectra are well fit by an absorbed power law model, as expected for a population dominated by blazars and pulsars. A small subset of 7 X-ray sources have spectra unlike the power law expected from a blazar or pulsar and may be linked to coincident stars or background emission. We develop a multiwavelength machine learning classifier to categorize unassociated sources into pulsars and blazars using gamma- and X-ray observations. Training a random forest procedure with known pulsars and blazars, we achieve a cross-validated classification accuracy of 98.6%. Applying the random forest routine to the unassociated sources returned 126 likely blazar candidates (defined as $P_{bzr}>90\%$) and 5 likely pulsar candidates ($P_{bzr}<10\%$). Our new X-ray spectral analysis does not drastically alter the random forest classifications of these sources compared to previous works, but it builds a more robust classification scheme and highlights the importance of X-ray spectral fitting. Our procedure can be further expanded with UV, visual, or radio spectral parameters or by measuring flux variability.

arXiv comment11 pages text, 4 figures, 4 tables (2 in-text, 2 in 11-page appendix), accepted for publication in the Astronomical Journal

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• Probing the origin of 4FGL J0822.8-4207: cosmic ray illumination from the SNR Puppis A and the Herbig-Haro object HH219
• A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources
• Chandra X-ray Observatory Observations of 13 Fermi LAT Sources
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• Classification of a New X-ray Catalog of Likely Counterparts to 4FGL-DR4 Unassociated Gamma-ray Sources Using a Neural Network