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.

瞬态噪声（毛刺）的存在阻碍了引力波的观测。我们研究高级 LIGO 探测器第三次观测运行的数据，并确定新的故障类别：快速散射/冠和低频光点. 使用通过监测探测器状态和公民科学志愿者组装的训练集，我们更新了 Gravity Spy 机器学习算法以进行故障分类。我们发现与探测器站点的地面运动相关的快速散射/冠特别普遍，并确定了与不同类型地面运动相关的两个子类。基于更新模型的数据重新分类发现，LIGO Livingston 大约 27% 的瞬态噪声属于快速散射类，而 8% 属于低频 blip 类，使它们成为最常见和第四常见的来源该站点的瞬态噪声。我们的结果展示了毛刺分类如何揭示引力波探测器的潜在改进，以及如何在给定适当的框架的情况下，