论文标题
全球最佳且可扩展的$ n $ - 天文学目录匹配
Globally optimal and scalable $N$-way matching of astronomy catalogs
论文作者
论文摘要
在以前的贝叶斯方法的基础上，我们引入了一种新颖的概率交叉识别表述，其中检测以全球最佳方式与（假设的）天文对象直接相关。我们表明，这种新方法比列举所有可能的候选人，尤其是在拥挤的领域的极限上，对于处理多个目录的范围要好，这是对新一代天文学实验（例如鲁宾天文对间的空间和时间（LSST））的最具挑战性的观察性制度（LSST）。在这里，我们研究了模拟目录，其中已知地面真相并报告了该方法的统计和计算性能。该论文伴随着公共软件工具，可根据方向数据执行全球最佳目录匹配。
Building on previous Bayesian approaches, we introduce a novel formulation of probabilistic cross-identification, where detections are directly associated to (hypothesized) astronomical objects in a globally optimal way. We show that this new method scales better for processing multiple catalogs than enumerating all possible candidates, especially in the limit of crowded fields, which is the most challenging observational regime for new-generation astronomy experiments such as the Rubin Observatory Legacy Survey of Space and Time (LSST). Here we study simulated catalogs where the ground-truth is known and report on the statistical and computational performance of the method. The paper is accompanied by a public software tool to perform globally optimal catalog matching based on directional data.
