A network of three or more gravitational wave detectors simultaneously taking data is required to generate a well-localized sky map for gravitational wave sources, such as GW170817. Local seismic disturbances often cause the LIGO and Virgo detectors to lose light resonance in one or more of their component optic cavities, and the affected detector is unable to take data until resonance is recovered. In this paper, we use machine learning techniques to gain insight into the predictive behavior of the LIGO detector optic cavities during the second LIGO-Virgo observing run. We identify a minimal set of optic cavity control signals and data features which capture interferometer behavior leading to a loss of light resonance, or lockloss. We use these channels to accurately distinguish between lockloss events and quiet interferometer operating times via both supervised and unsupervised machine learning methods. This analysis yields new insights into how components of the LIGO detectors contribute to lockloss events, which could inform detector commissioning efforts to mitigate the associated loss of uptime. Particularly, we find that the state of the component optical cavities is a better predictor of loss of lock than ground motion trends. We report prediction accuracies of 98% for times just prior to lock loss, and 90% for times up to 30 seconds prior to lockloss, which shows promise for this method to be applied in near-real time to trigger preventative detector state changes. This method can be extended to target other auxiliary subsystems or times of interest, such as transient noise or loss in detector sensitivity. Application of these techniques during the third LIGO-Virgo observing run and beyond would maximize the potential of the global detector network for multi-messenger astronomy with gravitational waves.

中文翻译：

---

评估和改进 LIGO 探测器占空比的新方法

需要一个由三个或更多引力波探测器同时采集数据的网络来为引力波源（例如 GW170817）生成定位良好的天空图。局部地震扰动通常会导致 LIGO 和 Virgo 探测器在其一个或多个组件光腔中失去光共振，并且受影响的探测器在共振恢复之前无法获取数据。在本文中，我们使用机器学习技术深入了解第二次 LIGO-Virgo 观测运行期间 LIGO 探测器光学腔的预测行为。我们确定了一组最小的光腔控制信号和数据特征，它们捕获了导致光共振损失或锁定损失的干涉仪行为。我们使用这些通道通过有监督和无监督的机器学习方法准确区分失锁事件和安静的干涉仪操作时间。这种分析产生了关于 LIGO 探测器的组件如何导致失锁事件的新见解，这可以为探测器调试工作提供信息，以减轻相关的正常运行时间损失。特别是，我们发现组件光腔的状态比地面运动趋势更能预测失锁。我们报告锁定丢失之前时间的预测准确率为 98%，锁定丢失前长达 30 秒的时间为 90%，这表明该方法有望近实时地应用于触发预防性检测器状态变化。这种方法可以扩展到针对其他辅助子系统或感兴趣的时间，例如瞬态噪声或检测器灵敏度的损失。在第三次 LIGO-Virgo 观测运行及以后应用这些技术将最大限度地发挥全球探测器网络在引力波多信使天文学方面的潜力。