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Kerby, Stephen; Kaur, Amanpreet; Falcone, Abraham D.; Stroh, Michael C.; Ferrara, Elizabeth C.; Kennea, Jamie A.; Colosimo, Joseph
The Astronomical journal, 04/2021, Letnik: 161, Številka: 4Journal Article
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 seven 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-ray and X-ray observations. Training a random forest (RF) procedure with known pulsars and blazars, we achieve a cross-validated classification accuracy of 98.6%. Applying the RF routine to the unassociated sources returned 126 likely blazar candidates (defined as P bzr ≥ 90%) and five likely pulsar candidates ( P bzr ≤ 10%). Our new X-ray spectral analysis does not drastically alter the RF 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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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