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PreprintCC BY 4.0arXiv

Multiwavelength Classification of Chandra Sources in Omega Centauri

  • Steven Chen
  • Oleg Kargaltsev
  • Jeremy Hare
  • Hui Yang
  • Oleg Smirnov
  • Ian Heywood
  • Talon Myburgh
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Abstract

We present a comprehensive study of X-ray sources in the globular cluster NGC 5139 to date. Over 200 Chandra sources are cross-matched to multiwavelength catalogs from HST, JWST, and MeerKAT. We employ a machine-learning classification pipeline (MUWCLASS) for the first time in such a crowded stellar environment and introduce the globular-cluster-specific training dataset, which compiles previously classified sources from 25 Galactic globular clusters. Given the challenges presented by the high-density environment, we compare the MUWCLASS results with traditional classification methods. We confirm counterpart associations suggested in previous works and also identify new confident and candidate associations. We discuss the implications of our classification results in light of models of cluster dynamical evolution and binary formation/destruction.

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Cite

BibTeXRIS
BibTeX
@misc{chen2026multiwavelength,
  title = {{Multiwavelength Classification of Chandra Sources in Omega Centauri}},
  author = {Steven Chen and Oleg Kargaltsev and Jeremy Hare and Hui Yang and Oleg Smirnov and Ian Heywood and Talon Myburgh},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.30604},
  eprint = {2610.30604},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.30604},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Multiwavelength Classification of Chandra Sources in Omega Centauri
AU  - Steven Chen
AU  - Oleg Kargaltsev
AU  - Jeremy Hare
AU  - Hui Yang
AU  - Oleg Smirnov
AU  - Ian Heywood
AU  - Talon Myburgh
PY  - 2026
DA  - 2026/10/22
PB  - arXiv
DO  - 10.48550/arxiv.2610.30604
UR  - https://arxiv.org/abs/2610.30604
AB  - We present a comprehensive study of X-ray sources in the globular cluster NGC 5139 to date. Over 200 Chandra sources are cross-matched to multiwavelength catalogs from HST, JWST, and MeerKAT. We employ a machine-learning classification pipeline (MUWCLASS) for the first time in such a crowded stellar environment and introduce the globular-cluster-specific training dataset, which compiles previously classified sources from 25 Galactic globular clusters. Given the challenges presented by the high-density environment, we compare the MUWCLASS results with traditional classification methods. We confirm counterpart associations suggested in previous works and also identify new confident and candidate associations. We discuss the implications of our classification results in light of models of cluster dynamical evolution and binary formation/destruction.
ER  -