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

论文概要

研究领域: ML
作者: V. Samuel Pérez-Díaz, Vinay L. Kashyap, Joshua D. Ingram
发布时间: 2026-06-19
arXiv: 2506.14975

中文摘要

本文提出了一个框架，用于将 Chandra 源目录（CSC v2.1）中的 X 射线源与 Gaia 数据发布3中的光学源进行交叉匹配。与纯粹基于空间位置的方法不同，该框架使用源属性（如星等、颜色和距离）来识别真实对应体、检测偶然巧合，并在存在多个合理候选者时解决歧义。

研究团队使用 NWAY（一种考虑位置误差和源密度的贝叶斯交叉匹配框架）定义高置信度匹配的训练集。然后在两个目录的各种特征上训练梯度提升分类器（LightGBM）。

在约 254k 个独特的 X 射线源中，为约 113k 个源找到对应体，其中约 7k 个存在多个合理的对应体。对于约 20k 个基于分离的交叉匹配能找到匹配但本框架未找到对应体的源，其中一半被归因于偶然巧合。

在 Chandra Orion 超深项目（COUP）上的验证表明，机器学习匹配在不使用任何位置信息的情况下再现了 95% 的 NWAY 交叉匹配。研究团队发布了约 113k 个 Chandra-Gaia 对应体目录，以及约 7k 个替代匹配和约 20k 个歧义 NWAY 关联，支持未来对 Chandra 和 Gaia 均可探测源的人群研究。

原文摘要

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 classifier (LightGBM) on a variety of features from both catalogs. Of the ~254k unique X-ray sources, we find counterparts for ~113k sources, of which plausible multiple counterparts are found for ~7k. We find no counterparts for ~20k sources for which separation-based cross-matching does find a match, and attribute half of these to chance coincidences. We validate the pipeline on the Chandra Orion Ultradeep Project (COUP), where the machine-learning matches reproduce 95% of NWAY cross-matches without using any positional information. We release a catalog of the ~113k Chandra-Gaia counterparts, together with ~7k alternative matches and ~20k ambiguous NWAY associations, supporting future population studies of sources detectable by both Chandra and Gaia. We discuss limitations and provide a generalization of the framework that is applicable in other cross-matching scenarios.

自动采集于 2026-06-19

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