Detection Of Anomalies Amongst LIGO’s Glitch Populations With Autoencoders
Abstract
Gravitational wave (GW) interferometers are able to detect a change in distance of ~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 Laser Interferometer Gravitational-Wave Observatory Livingston in the fractal dimension (FD), 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 FD and provide a flexible framework for glitch discovery.
Recommended Citation
P. Laguarta and R. van der Laag and M. Lopez and T. Dooney and A. L. Miller and S. Schmidt and M. Cavaglia and S. Caudill and K. Driessens and J. Karel and R. Lenders and C. Van Den Broeck, "Detection Of Anomalies Amongst LIGO’s Glitch Populations With Autoencoders," Classical and Quantum Gravity, vol. 41, no. 5, article no. 055004, IOP Publishing, Mar 2024.
The definitive version is available at https://doi.org/10.1088/1361-6382/ad1f26
Department(s)
Physics
Keywords and Phrases
auxiliary channels; gravitational waves; machine learning
International Standard Serial Number (ISSN)
1361-6382; 0264-9381
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 IOP Publishing, All rights reserved.
Publication Date
07 Mar 2024
Comments
National Science Foundation, Grant PHY-0757058