Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors

TL;DR

This paper introduces a machine learning pipeline for clustering multivariate time series data from gravitational-wave detectors to help operators identify seismic states and diagnose operational issues more efficiently.

Contribution

It presents a novel features-based clustering method tailored for seismic data in gravitational-wave observatories, enhancing operational monitoring.

Findings

Effective identification of seismic states correlating with detector events

Improved automation in monitoring multiple data streams

Enhanced diagnostic capabilities for detector operators

Abstract

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.