Classification of gravitational-wave glitches via dictionary learning

Autor:	Llorens-Monteagudo, Miquel, Torres-Forné, Alejandro, Font, José A., Marquina, Antonio
Rok vydání:	2018
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology
Druh dokumentu:	Working Paper
DOI:	10.1088/1361-6382/ab0657
Popis:	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. Comment: 19 pages, 13 figues
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.03867 Zobrazit plný text záznamu View this record from Arxiv