The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning
arXiv:2606.19329v1 Announce Type: cross Abstract: We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify true counterparts, detect chance coincidences, and resolve ambiguities when multiple plausible candidates exist. We define a training set of high-confidence matches using NWAY, a Bayesian cross-matching framework that accounts for positional errors and source densities. We train a gradient-boosted cl
The increasing volume and complexity of astronomical data necessitate advanced techniques like machine learning to improve catalog cross-matching accuracy.
Accurate cross-identification of celestial objects across different wavelengths is fundamental for deeper astrophysical understanding and discovery, enhancing the utility of observatories like Chandra and Gaia.
Astronomers can now more reliably correlate X-ray sources with optical counterparts, leading to clearer associations and reducing ambiguities in large datasets.
- · Astrophysicists
- · Space observatories
- · Machine learning researchers
- · Traditional spatial cross-matching methods
Improved astronomical catalogs facilitate more precise studies of cosmic phenomena.
Enhanced data integration may accelerate discoveries in areas like black holes, neutron stars, and active galactic nuclei.
The methodology could be generalized to other multi-wavelength astronomical surveys, improving our overall map of the universe.
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Read at arXiv cs.LG