Detection of anomalies amongst LIGO’s glitch populations with autoencoders

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 rates of non-gaussian, transient bursts of noise, also known as glitches, which hinder the detection and parameter estimation of
short- and long-lived GW signals in the main detector strain. Glitches, come in a wide range of frequency-amplitude-time morphologies and may be caused by environmental or instrumental processes, so a key step towards their mitigation is to understand their population. Current approaches for their identification use supervised models to learn their morphology in the main strain with a fixed set of classes, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers. In this work, we present an unsupervised algorithm to find anomalous glitches. Firstly, we encode a subset of auxiliary channels from LIGO Livingston in the fractal dimension, which measures the complexity of the signal. For this aim, we speed up the fractal
dimension calculation to encode $1\,$h of data in $11 \, $s. Secondly, we learn the underlying distribution of the data using an autoencoder with cyclic periodic convolutions. In this way, we learn the underlying distribution of glitches and we uncover
unknown glitch morphologies, and overlaps in time between different glitches
and misclassifications. This led to the discovery of $6.6\%$ anomalies in the input
data. The results of this investigation stress the learnable structure of auxiliary channels encoded in fractal dimension and provide a flexible framework for
glitch discovery.