论文标题

搜索与单个观测站的二进制合并的重力波

A Search for Gravitational Waves from Binary Mergers with a Single Observatory

论文作者

Nitz, Alexander H., Dent, Thomas, Davies, Gareth S., Harry, Ian

论文摘要

我们提出了搜索合并紧凑型二元重力波源的搜索,该二进制重力波源仅产生仅出现在单个检测器中的信号。过去的分析在很大程度上依赖于多个检测器之间的巧合,以减少非粪便背景。但是,对于2015 - 2017年Ligo-Virgo观察运行的$ \ sim40 \%$,只有一个检测器正在运行。我们讨论了分配显着性和计算主要由单个检测器观察到的候选者的概率的困难,并建议使用噪声模型进行直接分辨率,鉴于观察到的数据,旨在提供保守评估的噪声模型。我们还描述了一种评估在观察多个检测器时在单个检测器中观察到的候选者的程序。我们将这些方法应用于开放式LIGO数据跨度2015-2017中的二进制黑洞(BBH)和二进制中子星(BNS)合并。我们搜索中最有希望的候选人是170817+03:02:46utc(天体物理起源的概率$ p _ {\ rm asto} \ sim 0.4 $):如果天体物理学,这与BBH合并一致,与主要质量$ 67 _ _ _ { - 15}^{ - 15}^{ - 15}^{ - +21}}分层合并起源。我们还将方法应用于GW190425的分析,并找到$ p _ {\ rm astro} \ sim 0.5 $,尽管此值高度取决于对噪声和信号模型的假设。

We present a search for merging compact binary gravitational-wave sources that produce a signal appearing solely or primarily in a single detector. Past analyses have heavily relied on coincidence between multiple detectors to reduce non-astrophysical background. However, for $\sim40\%$ of the total time of the 2015-2017 LIGO-Virgo observing runs only a single detector was operating. We discuss the difficulties in assigning significance and calculating the probability of astrophysical origin for candidates observed primarily by a single detector, and suggest a straightforward resolution using a noise model designed to provide a conservative assessment given the observed data. We also describe a procedure to assess candidates observed in a single detector when multiple detectors are observing. We apply these methods to search for binary black hole (BBH) and binary neutron star (BNS) mergers in the open LIGO data spanning 2015-2017. The most promising candidate from our search is 170817+03:02:46UTC (probability of astrophysical origin $p_{\rm astro} \sim 0.4$): if astrophysical, this is consistent with a BBH merger with primary mass $67_{-15}^{+21}\,M_{\odot}$, suggestive of a hierarchical merger origin. We also apply our method to the analysis of GW190425 and find $p_{\rm astro} \sim 0.5$, though this value is highly dependent on assumptions about the noise and signal models.

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