PROBABILISTIC CROSS-IDENTIFICATION IN CROWDED FIELDS AS AN ASSIGNMENT PROBLEM
PROBABILISTIC CROSS-IDENTIFICATION IN CROWDED FIELDS AS AN ASSIGNMENT PROBLEM
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DOI：
10.3847/0004-6256/152/4/86
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
T. Budavári;A. Basu
中科院分区：
文献类型：
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作者：
T. Budavári;A. Basu
交叉识别的突出挑战之一是多重性：在天空拥挤区域的检测往往与一个以上的相似可能性的候选协会。我们将由此产生的最大似然分割映射到离散数学的基本分配问题，并使用所谓的匈牙利算法有效地解决了组合优化领域中的双向目录级匹配。我们介绍的方法，证明其性能在一个模拟的宇宙中，真正的协会是已知的，并讨论了新的程序的适用性，大型调查。
One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning to the fundamental assignment problem of discrete mathematics and efficiently solve the two-way catalog-level matching in the realm of combinatorial optimization using the so-called Hungarian algorithm. We introduce the method, demonstrate its performance in a mock universe where the true associations are known, and discuss the applicability of the new procedure to large surveys.