Image-based deep learning for classification of noise transients in gravitational wave detectors

TL;DR

This paper presents a deep learning-based image classification pipeline using convolutional neural networks to automatically identify and classify transient noise events (glitches) in gravitational wave detector data, enhancing real-time detector characterization.

Contribution

It introduces a novel CNN-based method to classify glitches from time-frequency images, demonstrating high accuracy and fast processing suitable for online use.

Findings

High classification accuracy on simulated glitches

Fast processing times enable real-time detection

Effective differentiation of glitch types

Abstract

The detection of gravitational waves has inaugurated the era of gravitational astronomy and opened new avenues for the multimessenger study of cosmic sources. Thanks to their sensitivity, the Advanced LIGO and Advanced Virgo interferometers will probe a much larger volume of space and expand the capability of discovering new gravitational wave emitters. The characterization of these detectors is a primary task in order to recognize the main sources of noise and optimize the sensitivity of interferometers. Glitches are transient noise events that can impact the data quality of the interferometers and their classification is an important task for detector characterization. Deep learning techniques are a promising tool for the recognition and classification of glitches. We present a classification pipeline that exploits convolutional neural networks to classify glitches starting from their time-frequency evolution represented as images. We evaluated the classification accuracy on simulated glitches, showing that the proposed algorithm can automatically classify glitches on very fast timescales and with high accuracy, thus providing a promising tool for online detector characterization.