  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.
