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

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 expec...

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Bibliographic Details
Published in:The Astronomical journal 2021-04, Vol.161 (4), p.154
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:
Active galactic nuclei
Astronomy
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Blazars
CLASSIFICATION
GAMMA RADIATION
Gamma rays
Gamma-ray sources
Machine learning
Population studies
Power law
PULSARS
Spectral analysis
Spectrum analysis
STARS
X RADIATION
X ray sources
X ray spectra
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Summary: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.
ISSN:0004-6256
1538-3881
1538-3881
DOI:10.3847/1538-3881/abda53