Anomaly Detection in Gravitational Waves data using Convolutional AutoEncoders

TL;DR

This paper introduces a novel anomaly detection method using convolutional autoencoders to identify transient signals and glitches in gravitational wave data, offering an alternative to traditional matched filtering techniques.

Contribution

The study presents a generic anomaly detection approach with convolutional autoencoders for gravitational wave data, capable of identifying signals without relying on precise waveform models.

Findings

Effective detection of transient GW signals and glitches.

Applicable to real LIGO/Virgo datasets.

Provides a model-independent detection framework.

Abstract

As of this moment, fifty gravitational waves (GW) detections have been announced, thanks to the observational efforts of the LIGO-Virgo Collaboration, working with the Advanced LIGO and the Advanced Virgo interferometers. The detection of signals is complicated by the noise-dominated nature of the data. Conventional approaches in GW detection procedures require either precise knowledge of the GW waveform in the context of matched filtering searches or coincident analysis of data from multiple detectors. Furthermore, the analysis is prone to contamination by instrumental or environmental artifacts called glitches which either mimic astrophysical signals or reduce the overall quality of data. In this paper, we propose an alternative generic method of studying GW data based on detecting anomalies. The anomalies we study are transient signals, different from the slow non-stationary noise of the detector. Presented in the manuscript anomalies are mostly based on the GW emitted by the mergers of binary black hole systems. However, the presented study of anomalies is not limited only to GW alone, but also includes glitches occurring in the real LIGO/Virgo dataset available at the Gravitational Waves Open Science Center.