A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data

TL;DR

This paper applies deep transfer learning, especially VGG19, to identify glitches in gravitational wave data, achieving high accuracy and addressing class imbalance issues in the dataset.

Contribution

It introduces a transfer learning approach for glitch detection in gravitational wave data and compares multiple deep learning architectures.

Findings

VGG19 achieved the highest AUC-ROC of 0.9898.

Deep transfer learning effectively identifies glitches in noisy gravitational wave data.

Class imbalance impacts the performance of some algorithms.

Abstract

LIGO interferometer is considered the most sensitive and complicated gravitational experimental equipment ever built. Its main objective is to detect the gravitational wave from the strongest events in the universe by observing if the length of its 4-kilometer arms change by a distance 10,000 times smaller than the diameter of a proton. Due to its sensitivity, interferometer is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the gravitational-wave community as glitches. This study focuses on identifying those glitches using different deep transfer learning algorithms. The extensive experiment shows that algorithm with architecture VGG19 recorded the highest AUC-ROC among other experimented algorithm with 0.9898. While all of the experimented algorithm achieved a considerably high AUC-ROC, some of the algorithm suffered from class imbalance of the dataset which has a detrimental effect when identifying other classes.