{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-multiwavelength-machine-learning-approach","title":"A Multiwavelength Machine-learning Approach to Classifying X-ray Sources in the Fields of Unidentified 4FGL-DR4 sources","arxiv_id":"2403.05068","date":"2024-03-08","proceeding":null,"authors":["Hui Yang","Jeremy Hare","Oleg Kargaltsev"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2403.05068v2","url_pdf":"https://arxiv.org/pdf/2403.05068v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-multiwavelength-machine-learning-approach","repo_url":"https://github.com/muwclass/muwclass","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}