iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors
iDQ: Statistical inference of non-gaussian noise with auxiliary degrees of freedom in gravitational-wave detectors
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DOI：
10.1088/2632-2153/abab5f
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
R. Essick;P. Godwin;C. Hanna;L. Blackburn;E. Katsavounidis
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作者：
R. Essick;P. Godwin;C. Hanna;L. Blackburn;E. Katsavounidis
引力波探测器是非常灵敏的仪器，通常能够对新的天文现象进行突破性的观测。然而，他们也见证了非平稳，非高斯噪声，可能被误认为是天体物理源，降低检测置信度，或简单地从噪声数据中提取信号参数复杂化。为了解决这个问题，我们提出了iDQ，这是一个监督学习框架，可以仅基于对引力波不敏感的辅助自由度来自主检测引力波探测器中的噪声伪影。iDQ在两台LIGO干涉仪中的每一台上都以低延迟的方式在整个先进探测器时代运行，实时提供有关每次探测的宝贵数据质量信息。我们记录的算法，描述的统计框架和引力波搜索内可能的应用。特别是，我们构建了一个似然比测试，同时考虑到非高斯噪声伪影的存在，并利用观测到的引力波应变信号和数千个辅助自由度的信息。我们还提出了几个例子，iDQ的性能与现代干涉仪，显示iDQ的能力，自主再现已知的数据质量监视器和识别噪声文物没有标记的其他分析。
Gravitational-wave detectors are exquisitely sensitive instruments and routinely enable ground-breaking observations of novel astronomical phenomena. However, they also witness non-stationary, non-Gaussian noise that can be mistaken for astrophysical sources, lower detection confidence, or simply complicate the extraction of signal parameters from noisy data. To address this, we present iDQ, a supervised learning framework to autonomously detect noise artifacts in gravitational-wave detectors based only on auxiliary degrees of freedom insensitive to gravitational waves. iDQ has operated in low latency throughout the advanced detector era at each of the two LIGO interferometers, providing invaluable data quality information about each detection to date in real-time. We document the algorithm, describing the statistical framework and possible applications within gravitational-wave searches. In particular, we construct a likelihood-ratio test that simultaneously accounts for the presence of non-Gaussian noise artifacts and utilizes information from both the observed gravitational-wave strain signal and thousands of auxiliary degrees of freedom. We also present several examples of iDQ’s performance with modern interferometers, showing iDQ’s ability to autonomously reproduce known data quality monitors and identify noise artifacts not flagged by other analyses.