Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications
Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications
复制标题
数据质量达到先进 LIGO 第三次观测运行：重力间谍故障分类
DOI：
10.1088/1361-6382/acb633
复制
发表时间：
2023
影响因子：
3.5
通讯作者：
Crowston, K.
中科院分区：
文献类型：
--
作者：
Glanzer, J.;Banagiri, S.;Coughlin, S. B.;Soni, S.;Zevin, M.;Berry, C. P. L.;Patane, O.;Bahaadini, S.;Rohani, N.;Crowston, K.
了解引力波探测器中的噪声是探测和解释引力波信号的核心。毛刺是瞬时的非高斯噪声特征，可能有一系列环境和仪器原因。重力间谍项目使用机器学习算法，根据故障的时频形态对故障进行分类。所得到的一组分类毛刺可以用作探测器特征调查的输入，用于如何减轻毛刺，或数据分析研究，如何改善毛刺的影响。本文给出了先进激光干涉引力波天文台(LIGO)第三次观测结束前的重力间谍数据分析结果。我们将来自LIGO Hanford的233981个毛刺和来自LIGO Livingston的379805个毛刺分类为形态类。我们发现两个LIGO站点之间的毛刺分布不同。这凸显了针对每个引力波观测站分别进行数据质量研究的潜在需要。
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.