Transient glitch mitigation in Advanced LIGO data
Transient glitch mitigation in Advanced LIGO data
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DOI：
10.1103/physrevd.104.102004
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发表时间：
2021-08
影响因子：
5
通讯作者：
J. D. Merritt;B. Farr;R. Hur;B. Edelman;Z. Doctor
中科院分区：
文献类型：
--
作者：
J. D. Merritt;B. Farr;R. Hur;B. Edelman;Z. Doctor
“毛刺”--由LIGO和Virgo等引力波干涉仪收集的数据中的瞬态噪声伪影--是搜索和表征引力波信号的一个始终存在的障碍。由于一些具有类似于高质量，高质量比和极端自旋双黑洞事件的形态，它们限制了对此类来源的敏感性。它们还可以作为所有来源的污染物，需要在进行天体物理推断之前进行有针对性的缓解。我们提出了一个数据驱动的，经常遇到的故障类型的参数模型，使用概率主成分分析。作为参数化引力波信号模型的噪声模拟，它可以很容易地纳入现有的搜索和探测器表征技术。我们已经用开源的glitschen包实现了我们的方法。使用LIGO的目前最有问题的毛刺类型，“光点”和“托姆特”，我们证明了适度尺寸的参数模型可以构建和使用频率论和贝叶斯分析的有效缓解。
“Glitches” – transient noise artifacts in the data collected by gravitational wave interferometers like LIGO and Virgo – are an ever-present obstacle for the search and characterization of gravitational wave signals. With some having morphology similar to high mass, high mass-ratio, and extreme-spin binary black hole events, they limit sensitivity to such sources. They can also act as a contaminant for all sources, requiring targeted mitigation before astrophysical inferences can be made. We propose a data driven, parametric model for frequently encountered glitch types using probabilistic principal component analysis. As a noise analog of parameterized gravitational wave signal models, it can be easily incorporated into existing search and detector characterization techniques. We have implemented our approach with the open source glitschen package. Using LIGO’s currently most problematic glitch types, the ‘blip’ and ‘tomte’, we demonstrate that parametric models of modest dimension can be constructed and used for effective mitigation in both frequentist and Bayesian analyses.