论文标题
数据质量直至高级Ligo的第三次观察运行：重力间谍故障分类
Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
论文作者
论文摘要
了解重力波探测器中的噪声对于检测和解释重力波信号至关重要。故障是短暂的，非高斯的噪声特征，可以具有一系列环境和仪器的起源。重力间谍项目使用机器学习算法根据其时间频形态对故障进行分类。由此产生的一组分类故障可以用作有关如何减轻故障的探测器特征研究的输入，或者对如何减轻小故障影响的数据分析研究。在这里，我们介绍了重力间谍分析数据的结果，直到高级LIGO的第三次观察运行结束。我们将来自Ligo Hanford的233981毛刺和379805个小故障分类为形态学类别。我们发现，小故障的分布在两个Ligo位点之间有所不同。这突出了对数据质量研究的潜在需求，以分别针对每个重力波观测站进行量身定制。
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 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.
