Detection of anomalies amongst LIGO's glitch populations with autoencoders

TL;DR

This paper introduces an unsupervised autoencoder-based method utilizing fractal dimension encoding of auxiliary channels to identify and analyze unknown glitch anomalies in LIGO gravitational-wave data, improving glitch detection capabilities.

Contribution

The work presents a novel unsupervised framework combining fractal dimension encoding and autoencoders to detect unknown glitches using auxiliary channel data in LIGO.

Findings

Discovered anomalies in 6.6% of data

Uncovered unknown glitch morphologies

Enhanced glitch identification framework

Abstract

Gravitational-wave (GW) interferometers are able to detect a change in distance of $\sim$ 1/10,000th the size of a proton. Such sensitivity leads to large appearance rates of non-Gaussian transient noise bursts in the main detector strain, also known as glitches. These glitches come in a wide range of frequency-amplitude-time morphologies and are caused by environmental or instrumental processes, hindering searches for all sources of gravitational waves. Current approaches for their identification use supervised models to learn their morphology in the main strain, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers nor provide a flexible framework for novel glitch morphologies. In this work, we present an unsupervised algorithm to find anomalous glitches. We encode a subset of auxiliary channels from LIGO Livingston in the fractal dimension, a measure for the complexity of the data, and learn the underlying distribution of the data using an auto-encoder with periodic convolutions. In this way, we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of anomalies in $6.6 \%$ of the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in fractal dimension and provide a flexible framework to improve the state-of-the-art of glitch identification algorithms.