论文标题
在第三次观察过程中，从重力波检测器数据中减去故障
Subtracting glitches from gravitational-wave detector data during the third observing run
论文作者
论文摘要
地面重力波探测器的数据包含许多短期工具伪像，称为“小故障”。这些伪影的高速率反过来导致重叠的二元二元凝胶的重力波信号很大一部分。在Ligo-Virgo的第三次观察跑中，$ \ 20 \％的信号需要由于故障而导致某种形式的缓解措施。这是第一次观察到的毛刺减法作为Ligo-Virgo-Kagra数据分析方法的一部分，用于大量检测到的重力波事件。这项工作描述了识别故障的方法，决定缓解是否需要缓解的决策过程以及用于建模和减去故障的两种算法，贝叶斯波和GWSubtract。通过对两个事件的案例研究，GW190424_180648和GW200129_065458，我们评估了小故障减法的有效性，比较了相关的小故障模型中的统计不确定性，并确定这些故障亚收集方法中的潜在局限性。我们终于概述了从这项首要努力中学到的教训，以进行未来的观察。
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, $\approx 20\%$ of signals required some form of mitigation due to glitches. This was the first observing run that 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.
