Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
复制标题
DOI：
10.1088/1361-6382/ac1ccb
复制
发表时间：
2021-03
影响因子：
3.5
通讯作者：
S. Soni;C. Berry;S. Coughlin;M. Harandi;C. Jackson;Kevin Crowston;C. Osterlund;O. Patane;A. Katsaggelos;L. Trouille;V-G Baranowski;W. Domainko;K. Kamiński;M. A. L. Rodriguez;U. Marciniak;P. Nauta;G. Niklasch;R. Rote;B. T'egl'as;C. Unsworth;C. Zhang
中科院分区：
文献类型：
--
作者：
S. Soni;C. Berry;S. Coughlin;M. Harandi;C. Jackson;Kevin Crowston;C. Osterlund;O. Patane;A. Katsaggelos;L. Trouille;V-G Baranowski;W. Domainko;K. Kamiński;M. A. L. Rodriguez;U. Marciniak;P. Nauta;G. Niklasch;R. Rote;B. T'egl'as;C. Unsworth;C. Zhang
引力波的观测受到瞬态噪声（毛刺）的阻碍。我们研究了来自先进激光干涉引力波天文台（Advanced LIGO）探测器第三次观测运行的数据，并确定了新的毛刺类别：快速散射/冠状和低频脉冲。通过监测探测器状态以及公民科学志愿者所收集的训练集，我们更新了用于毛刺分类的“引力间谍”（Gravity Spy）机器学习算法。我们发现与探测器站点地面运动相关的快速散射/冠状现象尤为普遍，并确定了与不同类型地面运动相关的两个子类。基于更新后的模型对数据进行重新分类发现，在利文斯顿激光干涉引力波天文台，约27%的所有瞬态噪声属于快速散射类别，而约8%属于低频脉冲类别，这使它们成为该站点最常见和第四常见的瞬态噪声源。我们的研究结果既展示了毛刺分类如何揭示引力波探测器的潜在改进之处，也展示了在适当的框架下，公民科学志愿者如何能够在大型数据集中有所发现。
The observation of gravitational waves is hindered by the presence of transient noise (glitches). We study data from the third observing run of the Advanced LIGO detectors, and identify new glitch classes: fast scattering/crown and low-frequency blips. Using training sets assembled by monitoring of the state of the detector, and by citizen-science volunteers, we update the Gravity Spy machine-learning algorithm for glitch classification. We find that fast scattering/crown, linked to ground motion at the detector sites, is especially prevalent, and identify two subclasses linked to different types of ground motion. Reclassification of data based on the updated model finds that ∼27% of all transient noise at LIGO Livingston belongs to the fast scattering class, while ∼8% belongs to the low-frequency blip class, making them the most frequent and fourth most frequent sources of transient noise at that site. Our results demonstrate both how glitch classification can reveal potential improvements to gravitational-wave detectors, and how, given an appropriate framework, citizen-science volunteers may make discoveries in large data sets.