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Detection of gravitational-wave signals from binary neutron star mergers using machine learning
Authors
Alexander H. Nitz
Frank Ohme
Martin B. Schäfer
Publication date
1 January 2020
Publisher
College Park, MD : American Physical Society
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arXiv
Abstract
As two neutron stars merge, they emit gravitational waves that can potentially be detected by Earth-bound detectors. Matched-filtering-based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network-based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from nonspinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of ten per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 4 in sensitivity to signals with a signal-to-noise ratio between 8 and 15. However, this approach is not yet competitive with traditional matched-filtering-based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can be applied not only to machine-learning-based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches. © 2020 authors. Published by the American Physical Society
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MPG.PuRe
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Last time updated on 10/10/2020
Institutionelles Repositorium der Leibniz Universität Hannover
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Last time updated on 11/04/2021