Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time Series

The phenomenon of Gravitational Wave (GW) analysis has grown in popularity as technology has advanced and the process of observing gravitational waves has become more precise. Although the sensitivity and the frequency of observation of GW signals are constantly improving, the possibility of noise in the collected GW data remains. In this paper, we propose two new Machine and Deep learning ensemble approaches (i.e., ShallowWaves and DeepWaves Ensembles) for detecting different types of noise and patterns in datasets from GW observatories. Our research also investigates various Machine and Deep Learning techniques for multi-class classification and provides a comprehensive benchmark, emphasizing the best results in terms of three commonly used performance metrics (i.e., accuracy, precision, and recall). We train and test our models on a dataset consisting of annotated time series from real-world data collected by the Advanced Laser Interferometer GW Observatory (LIGO). We empirically show that the best overall accuracy is obtained by the proposed DeepWaves Ensemble, followed close by the ShallowWaves Ensemble.