A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources
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| Format: | Preprint |
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2024
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| _version_ | 1866914914527870976 |
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| author | Yang, Hui Hare, Jeremy Kargaltsev, Oleg |
| author_facet | Yang, Hui Hare, Jeremy Kargaltsev, Oleg |
| contents | A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources with signal-to-noise ratios >3 located within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory observations included in the Chandra Source Catalog 2.0. Recent improvements to our pipeline include astrometric corrections, probabilistic cross-matching to lower-frequency counterparts, and a more realistic oversampling method. X-ray sources are classified into eight broad predetermined astrophysical classes defined in the updated training data set, which we also release. We present details of the machine-learning classification, describe the pipeline improvements, and perform an additional spectral and variability analysis for brighter sources. The classifications give 103 plausible X-ray counterparts to 42 GeV sources. We identify 2 GeV sources as isolated neutron star candidates, 16 as active galactic nucleus candidates, seven as sources associated with star-forming regions, and eight as ambiguous cases. For the remaining 40 unIDed 4FGL sources, we could not identify any plausible counterpart in X-rays, or they are too close to the Galactic Center. Finally, we outline the observational strategies and further improvements in the pipeline that can lead to more accurate classifications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_05068 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources Yang, Hui Hare, Jeremy Kargaltsev, Oleg High Energy Astrophysical Phenomena A large fraction of Fermi-Large Area Telescope (LAT) sources in the fourth Fermi-LAT 14 yr catalog (4FGL) still remain unidentified (unIDed). We continued to improve our machine-learning pipeline and used it to classify 1206 X-ray sources with signal-to-noise ratios >3 located within the extent of 73 unIDed 4FGL sources with Chandra X-ray Observatory observations included in the Chandra Source Catalog 2.0. Recent improvements to our pipeline include astrometric corrections, probabilistic cross-matching to lower-frequency counterparts, and a more realistic oversampling method. X-ray sources are classified into eight broad predetermined astrophysical classes defined in the updated training data set, which we also release. We present details of the machine-learning classification, describe the pipeline improvements, and perform an additional spectral and variability analysis for brighter sources. The classifications give 103 plausible X-ray counterparts to 42 GeV sources. We identify 2 GeV sources as isolated neutron star candidates, 16 as active galactic nucleus candidates, seven as sources associated with star-forming regions, and eight as ambiguous cases. For the remaining 40 unIDed 4FGL sources, we could not identify any plausible counterpart in X-rays, or they are too close to the Galactic Center. Finally, we outline the observational strategies and further improvements in the pipeline that can lead to more accurate classifications. |
| title | A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources |
| topic | High Energy Astrophysical Phenomena |
| url | https://arxiv.org/abs/2403.05068 |