X-ray Spectra and Multiwavelength Machine Learning Classification for Likely Counterparts toFermi3FGL Unassociated Sources

• Published in: The Astrophysical journal 2021-03, Vol.161 (4)
• Main Authors: Kerby, Stephen, Kaur, Amanpreet, Falcone, Abraham D., Stroh, Michael C., Ferrara, Elizabeth C., Kennea, Jamie A., Colosimo, Joseph
• Format: Article
• Language: English
• Subjects: Astronomy; Astrophysics
• ISSN: 0004-637X; 1538-4357
• DOI: 10.3847/1538-3881/abda53

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 ave 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.