论文标题

来自大型调查的连续重力波候选物的密度聚集

Density-clustering of continuous gravitational wave candidates from large surveys

论文作者

Steltner, Benjamin, Menne, Thorben, Papa, Maria Alessandra, Eggenstein, Heinz-Bernd

论文摘要

搜索连续重力波的目标是从例如非轴对快速旋转中子星。广泛的调查通常需要明确搜索大量不同的波形,很容易超过$ \ sim10^{17} $模板。在这种情况下,出于实际原因,只有顶部,例如$ \ sim10^{10} $,结果将通过阶段的层次结构保存和跟进。这些候选人中的大多数并非完全独立于邻近的候选人,而是由于某些共同原因而引起的:波动,信号或干扰。通过明智地将候选人聚集在一起,源于同一根本原因,随后的随访变得更加有效。基于迭代的迭代发现,在过去的搜索中已采用了许多聚类算法。本文介绍的新聚类方法比以前的方法有了重大改进:对过度密度的形状不可知,非常有效,有效:在非常高的检测效率下,它具有99.99美元的噪声拒绝,$ 99.99 \%$,能够比在固定的范围更高的稳定性相比,能够更高的候选人群体比固定的距离更高。我们还演示了如何最佳选择聚类参数。

Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron stars. Broad surveys often require to explicitly search for a very large number of different waveforms, easily exceeding $\sim10^{17}$ templates. In such cases, for practical reasons, only the top, say $\sim10^{10}$, results are saved and followed-up through a hierarchy of stages. Most of these candidates are not completely independent of neighbouring ones, but arise due to some common cause: a fluctuation, a signal or a disturbance. By judiciously clustering together candidates stemming from the same root cause, the subsequent follow-ups become more effective. A number of clustering algorithms have been employed in past searches based on iteratively finding symmetric and compact over-densities around candidates with high detection statistic values. The new clustering method presented in this paper is a significant improvement over previous methods: it is agnostic about the shape of the over-densities, is very efficient and it is effective: at a very high detection efficiency, it has a noise rejection of $99.99\%$ , is capable of clustering two orders of magnitude more candidates than attainable before and, at fixed sensitivity it enables more than a factor of 30 faster follow-ups. We also demonstrate how to optimally choose the clustering parameters.

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