连续引力波搜索的自适应聚类方法
Adaptive clustering procedure for continuous gravitational wave searches
摘要：
在连续引力波的分层搜索中，候选者的聚类是一个重要的后处理步骤，因为它减少了在连续阶段跟踪的噪声候选者的数量[1][7][12]。以前的聚类过程将附近的候选者捆绑在一起，将它们归因于相同的根本原因（无论是信号还是干扰），基于预定义的聚类量。在本文中，我们提出了一个程序，适应集群卷的数据本身，并检查这种卷的一致性与预期的信号。这显著地提高了在固定检测阈值下的噪声抑制能力，并且在用于后续阶段的固定计算资源下，这导致总体上更敏感的搜索。这个新的程序被用于第一次Einstein@Home搜索来自先进LIGO探测器（O 1）的第一次科学运行的数据[11]。
英文摘要：
In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].
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