A LightGBM classifier trained on NWAY Bayesian matches identifies true Chandra-Gaia counterparts for 113k X-ray sources, flags 7k ambiguous cases, and attributes half of 20k separation-only matches to chance coincidences, validated at 95% on COUP without positional features.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Observational data-driven MHD simulations reproduced an X1.6 flare's onset and showed that photospheric velocity input extends prediction lead time beyond one hour.
citing papers explorer
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The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning
A LightGBM classifier trained on NWAY Bayesian matches identifies true Chandra-Gaia counterparts for 113k X-ray sources, flags 7k ambiguous cases, and attributes half of 20k separation-only matches to chance coincidences, validated at 95% on COUP without positional features.
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Predictability of a solar flare in May 2024 using observational data-driven MHD simulations
Observational data-driven MHD simulations reproduced an X1.6 flare's onset and showed that photospheric velocity input extends prediction lead time beyond one hour.