Efficient Gravitational-wave Glitch Identification from Environmental Data Through Machine Learning

TL;DR

This paper introduces a machine learning method that analyzes environmental and instrumental data channels to identify glitches in LIGO gravitational wave detectors, reducing false alarms and improving detection confidence.

Contribution

The novel approach leverages all environmental and detector data channels for glitch identification, surpassing previous methods limited to gravitational wave data alone.

Findings

Reduces false alarm rate in gravitational wave detection

Enhances LIGO detector sensitivity and reliability

Successfully monitors all environmental and instrumental channels

Abstract

The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below $10^{-19}$\,$m/\sqrt{Hz}$ variation---a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance in the gravitational wave signal of brief, loud noise artifacts called ``glitches,'' which are environmental or instrumental in origin but can mimic true gravitational waves and therefore hinder sensitivity. Currently they are primarily identified by analysis of the gravitational wave data stream. Here we present a machine learning approach that can identify glitches by monitoring \textit{all} environmental and detector data channels, a task that has not previously been pursued due to its scale and the number of degrees of freedom within gravitational-wave detectors. The presented method is capable of reducing the gravitational-wave detector network's false alarm rate and improving the LIGO instruments, consequently enhancing detection confidence.