基于特征学习的类地引力波检测与诊断
Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
摘要：
随着工程系统复杂性的增长，越来越需要能够检测、诊断甚至纠正不可避免地出现并且难以或不可能手动诊断和修复的瞬态异常的自动方法。我们文明中最敏感和最复杂的系统之一是探测器，它们搜寻由引力波引起的令人难以置信的微小距离变化-这种现象最初由阿尔伯特·爱因斯坦预测，是黑洞和深空其他大质量物体碰撞的结果，会在宇宙中出现和传播。这种检测器的极端复杂性和精度导致它们受到瞬态噪声问题的影响，这可能会大大限制它们的灵敏度和有效性。在这项工作中，我们提出了一种方法，可以检测和表征紧急瞬态异常的大规模复杂系统的演示。我们通过限制引力波发现的一个普遍问题来说明自动化解决方案的性能，精度和适应性：陆地起源的噪声伪影污染了引力波观测站的高灵敏度测量，并且可以模糊甚至模仿他们正在收听的微弱天体物理信号。具体来说，我们演示了高度可解释的卷积分类器如何自动学习从辅助检测器数据中检测瞬态异常，而无需观察异常本身。我们还说明了该模型的其他几个有用的功能，包括它如何执行自动变量选择，以减少数以万计的辅助数据通道，只有几个相关的;它如何识别预测这些通道中的异常的行为特征;以及它如何可以用来调查个别异常和与它们相关的通道。
英文摘要：
As engineered systems grow in complexity, there is an increasing need for automatic methods that can detect, diagnose, and even correct transient anomalies that inevitably arise and can be difficult or impossible to diagnose and fix manually. Among the most sensitive and complex systems of our civilization are the detectors that search for incredibly small variations in distance caused by gravitational waves -- phenomena originally predicted by Albert Einstein to emerge and propagate through the universe as the result of collisions between black holes and other massive objects in deep space. The extreme complexity and precision of such detectors causes them to be subject to transient noise issues that can significantly limit their sensitivity and effectiveness. In this work, we present a demonstration of a method that can detect and characterize emergent transient anomalies of such massively complex systems. We illustrate the performance, precision, and adaptability of the automated solution via one of the prevalent issues limiting gravitational-wave discoveries: noise artifacts of terrestrial origin that contaminate gravitational wave observatories' highly sensitive measurements and can obscure or even mimic the faint astrophysical signals for which they are listening. Specifically, we demonstrate how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data without needing to observe the anomalies themselves. We also illustrate several other useful features of the model, including how it performs automatic variable selection to reduce tens of thousands of auxiliary data channels to only a few relevant ones; how it identifies behavioral signatures predictive of anomalies in those channels; and how it can be used to investigate individual anomalies and the channels associated with them.
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