A convolutional neural network to distinguish glitches from minute-long gravitational wave transients

Abstract

Gravitational wave bursts are transient signals distinct from compact binary mergers that arise from a wide variety of astrophysical phenomena. Because most of these phenomena are poorly modeled, the use of traditional search methods such as matched filtering is excluded. Bursts include short ($<$10 seconds) and long (from 10 to a few hundreds of seconds) duration signals for which the detection is constrained by environmental and instrumental transient noises called glitches. Glitches contaminate burst searches, reducing the amount of useful data and limiting the sensitivity of current algorithms. It is therefore of primordial importance to locate and distinguish them from potential burst signals. In this paper, we propose to train a convolutional neural network to detect glitches in the time-frequency space of the cross-correlated LIGO noise. We show that our network is retrieving more than 95$\%$ of the glitches while being trained only on a subset of the existing glitch classes highlighting the sensitivity of the network to completely new glitch classes.

12 pages, 15 figures

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Cited by in corpus (3)

• Gravity Spy: Lessons Learned and a Path Forward
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Unveiling gravitational waves from core-collapse supernovae with MUSE