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Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector. 

Ground-based gravitational-wave (GW) detectors are a frontier large-scale experiment in experimental astrophysics. Given the elusive nature of GWs, the ground-based detectors have complex interacting systems made up of exquisitely sensitive instruments which makes them susceptible to terrestrial noise sources. As these noise transients - termed as glitches - appear in the detector's main data channel, they can mask or mimic real GW signals resulting in false alarms in the detection pipelines. Given their high rate of occurrence compared to astrophysical signals, it is vital to examine these glitches and probe their origin in the detector's environment and instruments in order to possibly eliminate them from the science data. In this paper we present a tensor factorization-based data mining approach to finding witness events to these glitches in the network of heterogeneous sensors that monitor the detectors and build a catalog which can aid human operators in diagnosing the sources of these noise transients. Available from: https://openreview.net/forum?id=O9q0ma6Oh5e