Finding the Origin of Noise Transients in LIGO Data with Machine Learning
Finding the Origin of Noise Transients in LIGO Data with Machine Learning
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DOI：
10.4208/cicp.oa-2018-0092
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发表时间：
2019-04-01
影响因子：
3.7
通讯作者：
Gill, Teerth
中科院分区：
文献类型：
--
作者：
Cavaglia, Marco;Staats, Kai;Gill, Teerth
诸如先进激光干涉引力波天文台（Advanced LIGO）和处女座（Virgo）等引力波探测器所收集的干涉数据的质量提升，对于引力波天体物理学的成功至关重要。引力波探测器对仪器敏感频段内具有特征频率的多种非天体物理源的干扰敏感。去除破坏数据流的非天体物理伪影，对于增加引力波探测的数量和统计显著性以及实现对数据的精确天体物理解释至关重要。机器学习已被证明是分析天文学及相关研究领域中大量复杂数据的有力工具。我们提出了两种基于随机森林和遗传编程算法的机器学习方法，可用于确定LIGO探测器中非天体物理瞬变的来源。我们使用在先进LIGO首次观测运行期间识别出的两类已知仪器源瞬变，表明这些算法能够成功识别实际干涉数据中非天体物理瞬变的来源，从而有助于减轻引力波搜索中的仪器和环境干扰。虽然本文所描述的数据集特定于LIGO，且所采用的具体程序也是独一无二的，但随机森林和遗传编程代码库以及将它们作为一种双机器学习方法应用的方式，完全可以应用于任何被认为是通过机械耦合产生噪声且噪声源尚未被发现的仪器。
Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the datasets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.