Classification of gravitational-wave glitches via dictionary learning

Abstract

We present a new method for the classification of transient noise signals (or glitches) in advanced gravitational-wave interferometers. The method uses learned dictionaries (a supervised machine learning algorithm) for signal denoising, and untrained dictionaries for the final sparse reconstruction and classification. We use a data set of 3000 simulated glitches of three different waveform morphologies, comprising 1000 glitches per morphology. These data are embedded in non-white Gaussian noise to simulate the background noise of advanced LIGO in its broadband configuration. Our classification method yields a 96% accuracy for a large range of initial parameters, showing that learned dictionaries are an interesting approach for glitch classification. This work constitutes a preliminary step before assessing the performance of dictionary-learning methods with actual detector glitches.

arXiv comment19 pages, 13 figues

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Cited by in corpus (12)

• Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning
• Application of dictionary learning to denoise LIGO's blip noise transients
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Using Deep Learning to Localize Gravitational Wave Sources
• Exploring gravitational-wave detection and parameter inference using Deep Learning methods
• Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams
• Classification of the core-collapse supernova explosion mechanism with learned dictionaries
• Noise reduction on single-shot images using an autoencoder
• No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
• Novel Deep Learning Approach to Detecting Binary Black Hole Mergers
• Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
• Extract non-Gaussian Features in Gravitational Wave Observation Data Using Self-Supervised Learning