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English(EN) The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

机器学习框架增强天文源匹配 · 跟踪 2 个源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个机器学习框架，以提高 Chandra 源目录和 Gaia 数据发布 3 之间天文源匹配的准确性。这种新方法除了空间邻近性外，还利用了星等和颜色等源属性来解决歧义并识别真正的对照项。该系统使用一种称为 LightGBM 的梯度提升分类器进行训练，成功识别了约 113,000 个 X 射线源的对照项，显著增强了研究可被两种仪器检测到的天体的能力。 AI

影响 这种机器学习方法可以提高天文学数据分析的准确性和效率，从而带来新的发现。

排序理由 该集群包含一篇 arXiv 预印本，详细介绍了一种用于天文学数据分析的新机器学习方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · V. Samuel P\'erez-D\'iaz, Vinay L. Kashyap, Joshua D. Ingram, David Fouhey, Juan Rafael Mart\'inez-Galarza, Pavlos Protopapas, Jeremy J. Drake, Dong-Woo Kim, Cecilia Garraffo ·

    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 dis…

  2. arXiv cs.LG TIER_1 English(EN) · Cecilia Garraffo ·

    The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

    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 chanc…

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