Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

• URL: http://arxiv.org/abs/2203.05086v1
• Date: Wed, 9 Mar 2022 23:39:41 GMT
• Title: Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning
• Authors: Robert E. Colgan, Zsuzsa M\'arka, Jingkai Yan, Imre Bartos, John N. Wright, and Szabolcs M\'arka
• Abstract summary: We present a demonstration of a method that can detect and characterize emergent transient anomalies of massively complex systems. One of the prevalent issues limiting gravitational-wave discoveries is the noise artifacts of terrestrial origin. We show how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data.
• Score: 0.7388859384645262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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