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Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning

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.

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. They are also subject to nearly constant development, improvement, commissioning and other invasive actions that change the nature of the data and its artifact contamination. 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. The solution outlined is broadly applicable from medical to automotive and space systems, enabling automated anomaly discovery and characterization and human-in-the-loop anomaly elimination.

I. INTRODUCTION
Gravitational-wave detectors such as KAGRA, Virgo, GEO600, and Laser Interferometer Gravitational-wave Observatory (LIGO) use highly sensitive interferometers [1][2][3][4] to measure the extraordinarily tiny variations gravitational waves cause as they pass through the detectors. Discoveries [5][6][7] of gravitational-wave detectors are documented in extensive catalogs, such as GWTC-1 [8], GWTC-2 [9], GWTC-2.1 [10], GWTC-3 [11], 1-OGC [12], 2-OGC [13], 3-OGC [14], IAS-Princeton [15][16][17], and eccentric localizations [18]. Due to the detectors' precision, they are also subject to various forms of noise that can appear in the gravitational-wave measurement data and interfere with their ability to detect and characterize gravitational waves. Among the most troublesome forms of noise are short, loud anomalies known within LIGO as "glitches," which can obscure or even mimic real gravitational waves. Unlike ever-present background noise [19,20], glitches cannot simply be subtracted from the desired signal. They have a wide variety of causes, some of which are well-understood and many of which are not. Attempts to mitigate various types of glitches have been a significant focus of LIGO's engineering ef-forts for more than a decade, and have included multiple machine learning-based approaches . In addition to the main interferometer data channel sensitive to gravitational waves, known as the "strain," each LIGO detector continuously records hundreds of thousands of channels of "auxiliary" data describing various aspects of the detector's state and internal and external environment. Although the vast majority of these channels do not record useful information, some have been found to measure behavior predictive of or associated with certain types of glitches. Understanding which channels may be associated with glitches and how is therefore an important avenue for diagnosing the root causes of glitches and in some cases reducing or eliminating them.
Recent machine learning-based approaches to glitch understanding and mitigation include works that investigate the association between glitches and LIGO's auxiliary data channels by casting glitch detection as a classification problem using only non-astrophysicallysensitive auxiliary channel data as input [42,43]. Many other glitch detection methods directly analyze the gravitational-wave strain [21, 23, 25, 30-32, 34-39, 44-49]. However, models trained to replicate their results using only auxiliary channels can provide strain-independent corroboration of their results. Because such models base their predictions only on astrophysicallyinsensitive auxiliary data, they present a lower risk of inadvertently labeling an astrophysical event as a glitch. Notably, achieving a sufficient level of accuracy with a strictly auxiliary channel-based model necessarily implies that it has recognized and learned to be sensitive to behavior observed in only the auxiliary channels that is predictive of glitches in the gravitational-wave strain data stream. These automatically identified associations can then be analyzed by detector domain experts for potentially unexplored insights into the origins and causes of glitches. This pursuit could ultimately lead to mitigations that reduce glitch frequency, increase the sensitivity of the detector, and enable sufficiently confident detections of astrophysical events that would otherwise have been rejected as too uncertain [42].
In this work, we demonstrate and apply one such model to a real dataset of a certain type of glitch that was particularly prevalent at LIGO Livingston Observatory (LLO) at various times during LIGO's Observing Run 3b (O3b). We demonstrate the model's ability to identify such glitches with a remarkable accuracy greater than 97%. We also illustrate clearly how it arrives at its predictions and highlight several other useful aspects of the model, including its high degree of interpretability and the insights it can provide into the glitches and channels analyzed. The results provide a demonstration of the capabilities of the models described. We hope they will stimulate further exploration and application to additional glitch types and datasets, eventually leading to breakthroughs that allow glitches to be reduced or eliminated and the detectors' sensitivity to be increasedwhich could enable sufficiently confident detections of more gravitational waves and further our understanding of our universe.

II. FEATURE LEARNING FOR TRANSIENT ANOMALIES
Machine learning models have been demonstrated in recent works [42,43] to be able to learn to predict the presence or absence of glitches in high-dimensional gravitational-wave astronomy data by considering only auxiliary channel information, without looking at the gravitational-wave strain data stream in which the glitches themselves appear. In this section, we describe one such model initially proposed in [43], which we refer to as LF. The model is highly interpretable and wellsuited to efficient strain-independent glitch detection, offering several useful features including: • (i) Strain-independent glitch prediction. It is extremely important for potential gravitationalwave detections to be rigorously vetted to ensure they are in fact true astrophysical events. Terrestrial-origin glitches can obscure or mimic gravitational waves, so it is also important to identify and distinguish them from astrophysical signals with high confidence. Unlike existing glitchdetection methods such as Omicron [50] and Grav-itySpy [21] that directly analyze the gravitationalwave strain data stream to search for high-power transient noise events, the methods discussed here consider only the detector's auxiliary data channels. As such, they provide independent corroboration from an entirely different data source; for example, because auxiliary channels are not astrophysically sensitive, it is unlikely that these methods would inadvertently identify a true gravitational wave as a glitch. [Section IV A] • (ii) Channel selection. The models are trained on tens of thousands of auxiliary channels. Certain channels are known to detector experts to monitor aspects of the detector's behavior associated with glitches, but it would be impossible to comprehensively analyze all channels manually. Absent such domain expert knowledge and time, machine learning models can discover connections between auxiliary channels and glitches that might otherwise have gone undetected. Notably, the models discussed here are explicitly tuned to learn to ignore input channels that are not useful for making its predictions-the vast majority of them, in this setting. Such a practice has previously been demonstrated (e.g., in [42,43]) to improve both interpretability and accuracy. These methods achieve excellent performance while ignoring all but a few dozen to few hundred channels, which can then be further investigated manually by detector domain experts.
[Section IV B] • (iii) Learned features. In addition to simply telling us which channels to look at, the models discussed here also learn behavioral signatures that correspond to patterns of behavior associated with glitches (or the lack thereof) in individual channels. For example, if a certain type of glitch is frequently preceded by a level change in a particular channel, we can expect the feature corresponding to that channel to learn to be sensitive to such a pattern. A few of these learned features are illustrated in Figs For the experiments presented here, we employ a slightly modified version of the LF model introduced in [43]. The model is a flat convolutional binary classifier parameterized by a learned filter f p ∈ R T of desired length T 1 for each of the P input time series (auxiliary channels) as well as a single learned bias term b sp for all channels of each sample rate s p . 2 To make a prediction at time t, the model takes as input the normalized data (centered at t) for all channels x p [t] ∈ R T of each sample rate s p ; cross-correlates the data with the corresponding filter f p ; sums the results for each sample rate s p and adds the corresponding bias term b sp ; and sums the results across all sample rates. The result is then translated into a probability estimate between 0 and 1 (where 0 indicates the model is certain no glitch is present and 1 indicates it is certain there is a glitch) by passing it through a sigmoid function. Mathematically, where σ denotes the logistic function σ(x) = (1 + exp(−x)) −1 and denotes discrete correlation.
As in [43], we employ a sparsifying regularizer-the elastic net [51]-during training to encourage most of the filters to be 0, i.e. f p = 0 for most p, because sparsity was found to be essential to good performance as well as beneficial to interpretability [42,43].
After training, the remaining nonzero filters correspond to the channels the model finds useful in making its predictions, indicating channels with behavior that was associated with the appearance of glitches or lack thereof during the training period. The magnitude of the filters also provides an indication of the relative importance of each channel to the model's predictions (intuitively, when correlated with a higher-magnitude filter, an input data segment will contribute more heavily to the sum and resulting probability estimate than the same segment correlated with a lower-magnitude filter) [43].
In Figs. 1 and 2, we illustrate visually how the model arrives at its predictions given a potential glitch time t. Fig. 1 shows two examples of learned filters f p . Fig. 2 illustrates two channels' data x p [t] (blue) during several glitches around GPS time t = 1, 259, 253, 345 (the center of the x-axis) as well as the cross-correlation (orange) between the channel data centered at each point on the x-axis and the corresponding filter illustrated in Fig. 1. To arrive at an estimate that there is a glitch present at that time, the model would sum the cross-correlations at that time over all channels considered. In Fig. 2, the cross-correlation values for both channels at that time are positive, so both channels are contributing to the model predicting the presence of a glitch. The left channel's cross-correlation is significantly greater, however, so it makes a larger contribution to the model's confidence that a glitch is present.

III. APPLICATION TO SCATTERED LIGHT GLITCHES
To illustrate how this method can be employed to interrogate a particular variety of recurring glitch, in this section we demonstrate its application to a particularly troublesome glitch type known as "scattered light" [54][55][56][57][58][59][60] during a period when the LLO detector was experiencing an elevated frequency of this type of glitch. We chose this particular class of glitch to validate our machine learning and auxiliary channel-based approach because it represents a well-characterized area that has been studied with multiple different approaches [21,54].

A. Scattered Light Glitches
On December 1, 2019, the LLO detector experienced a high level of scattered light glitches, one of which is illustrated in a time-frequency plot in Fig. 3. Scattering is observed as wide "arches" on the Omegagram, a time-frequency representation of the gravitational-wave channel. These glitches pollute the low-frequency regime (<100Hz) of the strain data. This lowest frequency part of the LIGO sensitivity region is observationally important, as the highest-mass binary black holes observed by LIGO merge in this regime. As a result, LIGO observed an increased trigger rate in the output of compact binary coalescence pipelines, especially for short duration templates [61]. Recognizing, characterizing, and mitigating scattered light glitches thus can significantly increase the confidence in LIGO gravitational-wave detections.
Increased scattered light glitch rates have been observed to correlate with increased microseismic activity at the LIGO detector sites [54,61]. The "microseism" band refers to the 0.03-0.5 Hz region in the amplitude spectral density curve of the observed ground motion produced by ocean waves. LLO generally experiences increased microseismic activity due to its close proximity to the Gulf of Mexico. The microseismic noise at LLO usually peaks approximately between 0.1-0.3 Hz. This frequency is below the dominant pendulum frequency of the quadruple suspension (0.45Hz [54]); therefore, any force applied below this frequency will move the end test mass suspension chain together and allow spurious opti-  . The y-axis corresponds to standard deviations above or below the mean (computed over all samples during the training period). Orange: the result of the cross-correlation between the filters shown in Fig. 1 and the raw channel data over time. At each point along the x-axis, the filter is cross-correlated with the corresponding number of samples of raw data centered at that point; the result is plotted on the y-axis. Note that the filter for the channel on the right is negative, so when the raw value drops below 0 the cross-correlation is positive. Fig. 7 shows a visualization of the strain data during the same time period illustrated above-in which the glitches are clearly visible-as well as the model's final glitch probability estimate over that period.
cal noise to be fed back to the interferometer's output, giving rise to scattering arches.

B. Data for Glitch Analysis
We obtained raw auxiliary channel data from the 70,000-second (17 hour, 40 minute) period from GPS times 1,259,197,952 to 1,259,267,952 (December 1, 2019, 1:12:14 to 20:38:54 UTC). During that period, the Omicron glitch detector [50], which monitors the gravitational-wave strain data for events of excess power, recorded a total 48,879 glitches of signal-to-noise ratio (SNR) 5 or greater. The GravitySpy project [21] selects a subset of high-SNR Omicron glitches that are wellsuited to morphological classification through a combi-nation of convolutional neural network (CNN)-based machine learning and citizen science. Of the 6,515 glitches classified in total by GravitySpy during that period, 5,499 were classified as "scattered light." 3 There were several breaks in lock during the above period; we discarded data that fell outside of a lock segment.
We draw training data from the 50,000-second period from GPS times 1,259,197,  training, validation, and test periods respectively.
We follow the same procedure as [42] and [43] to reduce the number of auxiliary channels considered from approximately 250,000 to approximately 40,000 by excluding channels that are constant or only vary in a predictable fashion (e.g., counting time cycles). We also exclude any auxiliary channels known or suspected to be coupled to the gravitational-wave data stream following the procedure of [42]. We include all remaining channels at all sample rates (a total of 39,147). We normalize each channel by computing the mean and standard deviation of the raw channel data over the entire training data period; then we subtract the training mean and divide by the standard deviation for all data in the training, validation, and test periods.
Our positive samples are drawn from points in time identified by GravitySpy [21] as scattered light glitches.
As in [42] and [43], our negative samples are drawn randomly from periods where no glitch was identified by the Omicron glitch detector [50] within two seconds. We select the same number of negative samples as there are positive samples in each dataset.

IV. RESULTS
In this section we describe our findings upon applying the LF model to the scattered light data described in Sec. III. We note that that neither the model nor the authors incorporated any specific prior knowledge of the characteristics of scattered light glitches nor the input auxiliary channels; these results were achieved independently and automatically by the method presented here.
The guishing between glitches and glitch-free points from our test dataset. It is able to achieve this level of accuracy after being trained on data from all 39,147 potentially informative auxiliary channels and automatically selecting only 25 of them (Table I) as relevant to predict this type of glitch. For each selected channel, the model also provides a visually interpretable feature for each selected channel, suggesting a signature of behavior in that channel associated with the presence or absence of a scattered light glitch.
These results and their potential interpretations may suggest novel directions for investigation of the origins and causes of scattered light glitches deserving of deeper investigations by instrument science domain experts.

A. Predictive Accuracy
The best-performing (by loss) model on the scattered light validation dataset achieved a loss of 0.0933, corresponding to an accuracy of 97.0% (true positive rate 97.4%, true negative rate 96.6%). On the test dataset, it achieved a loss of 0.0866, corresponding to an accuracy of 97.1% (true positive rate 98.4%, true negative rate 95.8%). An ROC curve for the test dataset is shown in Fig. 5.
Additionally, we manually examined Omegagrams around the 43 instances classified as a glitch by our model but not by GravitySpy or Omicron from our test dataset and found faint but clear signatures of possible scattered light glitches in as many as 30 of them. If we excluded those 30 from the 1,016 samples labeled "glitch-free" in our ground truth, our model's true negative rate would rise from 95.8% to 98.7% and overall accuracy would rise from 97.1% to 98.6%. We show an example of one such false positive by our model in Fig. 6.
For comparison, we also performed an experiment with the same type of model and with data drawn from the same periods as for the scattered light classifier (as described in Sec. III A) but using all Omicron glitches as the positive class rather than scattered light glitches only. It achieved a maximum validation accuracy of 82.8%, indicating that scattered light glitches are significantly easier to classify than general Omicron glitches.
As an example of how the model's output varies over time depending on the presence or absence of a glitch, we fed it a continuous segment of auxiliary channel data around several scattered light glitches that occurred around GPS time 1,259,253,345. Fig. 7 shows an Omegagram [52,53] of these glitches (top) and our model's estimate of the probability of a glitch at any given time (bottom). GravitySpy reports a 19.75-second scattered light glitch centered at 1,259,253,341.438 (35 seconds on the x-axis of Fig. 7), which our model classified as a glitch with very high confidence. GravitySpy also reports several other shorter scattered light glitches around that time, which are visible in the Omegagram and reflected in our model's output.
We illustrate the Omegagrams of two more scattered light glitches along with the model's output over the surrounding time period in Figs. 8 and 9. We illustrate the corresponding raw channel value and correlation for selected channels in Figs. 13 and 14 (see Sec. IV D). In Fig. 10, we show the Omegagram of a glitch-free point along with the model's output over the surrounding time period; in Fig. 15, we also show the corresponding raw channel value and correlation for the same channels as shown in Fig. 12

B. Selected Channels
The nonzero channels from the best-performing scattered light model and the magnitudes of their associated learned filters are listed in Table I than zero.
The magnitude of the filters also provides an indication of the relative importance of each channel to the model's predictions (intuitively, when correlated with a higher-magnitude filter, an input data segment will contribute more heavily to the sum and resulting probabil-  ity estimate than the same segment correlated with a lower-magnitude filter) [43]. Among those 25, the magnitudes decay rapidly, suggesting the model's predictions are dominated by the contributions of even fewer channels.

C. Learned Filters
Several of the learned filters are illustrated in Fig. 11. We leave detailed interpretive analysis and instrument hardware-related detective work regarding the channels and features themselves to future work. However, we qualitatively observe that many of the channels have a similar rising oscillatory shape. All four of the highestmagnitude channels listed in Table I, the first two of which are shown in the top left and top center of Fig.  1, are nearly identical in shape, likely indicating that they are sensitive to the same or similar phenomena; this is also unsurprising given the similarity of their names. We also note that the period of oscillation in the features displaying that pattern is of a similar (3-4 second) duration to the individual arches characteristic of scattered light glitches, perhaps suggesting that these channels are sensitive to the same phenomena that result in the appearance of this type of glitch in the strain. Fig. 12 shows the normalized value of several auxiliary channels during that span (blue) as well as the correlation between the channel and the model's learned filter (orange) at each time sample during that span. A pos-  Table I), although we omit some channels for illustration (for example, all of the first four highest-magnitude filters are nearly identical in shape). The x-axes correspond to filter length in seconds; the y-axes show the magnitude of the filter over time. Intuitively, a higher overall magnitude indicates the associated channel is more important to the model's decisions: when cross-correlated with a higher-magnitude filter, an input data segment will contribute more heavily to the sum and resulting probability estimate than the same segment cross-correlated with a lower-magnitude filter.

D. Channel Importance for Individual Glitch Classification
itive value for the correlation at a given time indicates the channel is contributing to the model's classification of the time as containing a glitch (the higher the value the more heavily weighted the contribution), and a negative value indicates the channel is indicating no glitch is present.
We illustrate similar examples with different glitches in Figs. 13 and 14 and an example with a glitch-free point (the same glitch-free point shown in Fig. 10) in Fig. 15.

E. Interpretations
The most significant channels in Table I refer to the third stage (from top) of the quadruple suspension system [67][68][69][70][71][72][73][74] at the X end station (SUS-ETMX L2). Figs. 16 and 17 show the test mass chain and the reaction mass chain behind it. Precision position sensors (socalled OSEMs [69,[75][76][77][78][79]) provide the optical sensing and electromagnetic actuating capabilities for the upper three stages. L2 refers to the third (penultimate) stage above the test mass and the bottom mass of the reaction chain. UL, UR, LL, LR refer to the sensors at the four quadrants of the penultimate mass of the reaction chain. The band-limited RMS motion for these sensors is continuously monitored. We observe that the channels related to these sensors have the most significant non-zero filters; other channels at SUS-ETMX referring to upper stages of the suspension chain (R0, L1) are also indicated. Fig. 12 shows the peaks in the oscillations in the L1:SUS-ETMX channels are spaced approximately 3.5 seconds apart, similar to the spacing between the peaks of the characteristic scattered light arches in the corresponding Omegagram (Fig. 7). This suggests that motion indicated by the OSEMs at the end station suspension chain directly gives rise to the observation of scattered light arches in the gravitational-wave strain data channel. Key to taking strain data is controlling the in- terferometer arms in unison such that the optical cavities in the arms stay resonant and the differential arm length can be continuously sensed. The end test mass chain motion is "locked" to the motion of the other interferometric optics at the detector's km-scale, and thus its motion relative to other local surfaces in the same chamber can be enhanced, especially during times when high local ground motion is observed. Fig. 4 shows significantly increased (by approximately a factor of five) microseismic activity in the approximately 0.13-0.15 Hz range for the glitch data used in our studies.
This phenomenon was investigated in [54,80,81], and a scattering mechanism involving the bottom-most reaction mass was verified. This ring-shaped mass contains five highly reflective circular gold traces (used as electrostatic drive used for interferometer control). Any incoming stray light will be back-scattered towards the test mass with a phase shift; a fraction of that is transmitted into the interferometer arm through the test mass, where it can interfere with the main beam and manifest as an increase in strain amplitude. The path between the reaction mass and end test mass can be traversed multiple times (due to multiple reflections), causing the appearance of harmonics of scattering arches on the Omegagrams (see, for example, Fig. 3). Our study is in agreement with back-scattering from the bottom-most reaction mass being the main source of scattered light glitches in the gravitational-wave data stream. The four most significant channels associated with scattered light glitches directly measure motion of the penultimate reaction mass right above the indicated scattering surface; its motion is naturally connected to that of the scattering surface that can easily couple back to the interferometer arms. Raw channel data (blue) from around the scattered light glitch illustrated in Fig. 9, along with (orange) the output of that channel cross-correlated with the associated learned filter (i.e., its contribution to the classifier's result).

V. CONCLUSION
In this work we have demonstrated the application and capabilities of a machine learning algorithm that employs automatic feature learning to extract relevant information for detecting and analyzing gravitational-wave detector glitches from a vast collection of auxiliary data channels without the need for manual channel selection or feature engineering. Given tens of thousands of raw time series, the vast majority of which may be irrelevant to the task of glitch analysis, the model automatically selects the few most relevant ones and distills the most pertinent information into several key components that can be further analyzed by detector experts, providing a valuable new tool in their efforts to decrease the incidence of glitches and increase the detectors' sensitivity. In particular, this method provides accurate strainindependent glitch prediction, automatic channel selection, and per-channel behavioral signatures, as well as a straightforward way to investigate which channels are associated with an individual glitch or set of glitches.
The method performs exceptionally well on a real dataset of scattered light glitches from LLO, achieving over 97% accuracy in distinguishing them from glitchfree points. Beyond glitch identification alone, we show how it selects only 25 out of nearly 40,000 channels as relevant to distinguishing glitches from glitch-free times and how it automatically designs relevant features associated with glitches (or the lack thereof) for each of those channels. We also illustrate precisely how the model arrives at its prediction for a given time of interest by visualizing multiple real examples. Unlike some machine learning methods, the method is highly interpretable, allowing detector domain experts to clearly understand how and why it makes a particular individual prediction as well as to probe it for broader understanding of glitch-related behavior over a given time period. We find physical, detector-specific results with meaningful interpretations consistent with previous research on the possible origins of scattered light glitches and their association with microseismic activity near the detector.
Previous work [43] has also shown that deeper, more complex neural network-based models can improve performance. In addition to the LF model employed here, [43] also proposed deeper models incorporating nonlinearities, pooling, and other features and showed that they boost performance on a similar LIGO auxiliary channel dataset. However, more complex models are less straightforward to interpret. For the scattered light study presented here (Sec. IV), we found improved performance with the VGG13-BN model of [43]-test accuracy of 98.2% vs. 97.1%-but felt that the performance gain was not significant enough to outweigh the tradeoff to interpretability. For other applications or datasets, however, one might prefer to sacrifice some interpretability for improved accuracy. One direction for future work would be to investigate analytical means of interpreting such models applied to this type of data, especially in situations where performance is more critical or the performance gains are more significant. Additional directions for future work include evaluating the method on other types of glitches and over longer time periods. Previous work on related models has shown that they generally do not perform as well when asked to analyze data drawn from wider time periods, perhaps because they are not able to account well for long-term distribution shifts in the state of the detector. One potential avenue to mitigate this would be to identify and test classes of machine learning models that are explicitly designed to handle long-term time dependencies, such as recurrent or long short-term memory neural networks; another would be to retrain or update models like the one employed here online in real time.
We have quantitatively observed here and in previous works that not all glitch morphologies and classes can be equally well predicted based on the auxiliary channels. One intriguing explanation is that information critical to such glitches is not recorded by any current auxiliary channel. Absent such necessary information, it would be difficult or even impossible for any auxiliary channel-based method to detect them. Therefore, we can treat low-accuracy glitch classes as possible clues to additional auxiliary channels that could be added describing previously unmonitored aspects of the detector. Therefore the method can also be useful to help us understand what is missing from the auxiliary monitoring network and channels.
Complex systems of humanity have always been plagued with emergent transient issues that can predict or cause eventual failure. Timely detection, understanding, and repair are paramount to reliable and optimized system operation. Gravitational-wave detectors can serve as excellent test and development environments for generic solutions to unpredictable issues that inevitably emerge in such extremely complex, sensitive systems. The solution outlined above is broadly applicable from medical to automotive and space systems, enabling automated anomaly discovery, characterization, and human-in-the-loop anomaly elimination. The last step of automated anomaly elimination remains a grand challenge of the field, with the long-term goal of accelerating automated understanding of complex systems and eliminating their problems as they emerge.

ACKNOWLEDGMENTS
We acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010. This material is based upon work and data supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation. This research has also made use of data obtained from the Gravitational Wave Open Science Center (https://www. gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO is funded by the U.S. National Science Foundation. Virgo is funded by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale della Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by Polish and Hungarian institutes.
The authors are grateful for the LIGO Scientific Collaboration review of the paper, with special thanks to Gabriele Vajente. This paper is assigned a LIGO DCC number(LIGO-P2200045). The authors acknowledge the LIGO Collaboration for the production of data used in this study and the LIGO Laboratory for enabling Omicron trigger generation on its computing resources (National Science Foundation Grants PHY-0757058 and PHY-0823459). The authors are grateful to the authors and maintainers of the Omicron and Omega pipelines, the LIGO Commissioning and Detector Characterization Teams and LSC domain expert Colleagues whose fundamental work on the LIGO detectors enabled the data used in this paper. The authors would like to thank colleagues of the LIGO Scientific Collaboration and the Virgo Collaboration for their help and useful comments.
The authors thank the University of Florida and Columbia University in the City of New York for their generous support. The authors are grateful for the generous support of the National Science Foundation under grant CCF-1740391. I.B. acknowledges the support of the Alfred P. Sloan Foundation and NSF grants PHY-1911796 and PHY-2110060.