Deep Multi-view Models for Glitch Classification

TL;DR

This paper introduces a deep multi-view convolutional neural network for automatic classification of glitches in gravitational-wave data, improving accuracy by leveraging multiple spectrogram views.

Contribution

It presents a novel multi-view deep learning approach that enhances glitch classification accuracy over traditional single-view methods.

Findings

Improved classification accuracy with the multi-view model

Effective visualization of glitches as spectrograms

Demonstrated superiority over traditional algorithms

Abstract

Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this paper, we propose a deep multi-view convolutional neural network to classify glitches automatically. The primary purpose of classifying glitches is to understand their characteristics and origin, which facilitates their removal from the data or from the detector entirely. We visualize glitches as spectrograms and leverage the state-of-the-art image classification techniques in our model. The suggested classifier is a multi-view deep neural network that exploits four different views for classification. The experimental results demonstrate that the proposed model improves the overall accuracy of the classification compared to traditional single view algorithms.