Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run
Subtracting glitches from gravitational-wave detector data during the third LIGO-Virgo observing run
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DOI：
10.1088/1361-6382/aca238
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发表时间：
2022-07
影响因子：
3.5
通讯作者：
D. Davis;T. Littenberg;I. Romero-Shaw;M. Millhouse;J. McIver;F. Di Renzo;G. Ashton
中科院分区：
文献类型：
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作者：
D. Davis;T. Littenberg;I. Romero-Shaw;M. Millhouse;J. McIver;F. Di Renzo;G. Ashton
来自地面引力波探测器的数据包含许多短时间的仪器伪影，称为“毛刺”。这些伪影的高比率反过来又导致了来自致密双星聚结重叠毛刺的引力波信号的显著部分。在LIGO-Virgo的第三次观测中，由于故障，大约20%的引力波源候选者需要某种形式的缓解。这是第一次将毛刺扣除作为LIGO-Virgo-KAGRA数据分析方法的一部分，用于大部分检测到的引力波事件。这项工作描述了识别毛刺的方法，决定是否需要缓解的决策过程，以及用于建模和消除毛刺的两种算法BayesWave和gwsubtract。通过案例研究的两个事件，GW190424_180648和GW200129_065458，我们评估的毛刺减法的有效性，比较相关的毛刺模型的统计不确定性，并确定这些毛刺减法方法的潜在局限性。最后，我们概述了从这第一次的努力，为未来的观测运行的经验教训。
Data from ground-based gravitational-wave detectors contains numerous short-duration instrumental artifacts, called ‘glitches’. The high rate of these artifacts in turn results in a significant fraction of gravitational-wave signals from compact binary coalescences overlapping glitches. In LIGO-Virgo’s third observing run, ≈20% of gravitational-wave source candidates required some form of mitigation due to glitches. This was the first observing run where glitch subtraction was included as a part of LIGO-Virgo-KAGRA data analysis methods for a large fraction of detected gravitational-wave events. This work describes the methods to identify glitches, the decision process for deciding if mitigation was necessary, and the two algorithms, BayesWave and gwsubtract, that were used to model and subtract glitches. Through case studies of two events, GW190424_180648 and GW200129_065458, we evaluate the effectiveness of the glitch subtraction, compare the statistical uncertainties in the relevant glitch models, and identify potential limitations in these glitch subtraction methods. We finally outline the lessons learned from this first-of-its-kind effort for future observing runs.