Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical informat...

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Vydáno v:	Machine learning: science and technology Ročník 4; číslo 3; s. 35024 - 35044
Hlavní autoři:	Bacon, Philippe, Trovato, Agata, Bejger, Michał
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bristol IOP Publishing 01.09.2023 IOP Publishing Ltd
Témata:	Algorithms Artificial neural networks Black holes Broadband Coders convolutional neural network data analysis Data transmission Datasets denoising autoencoder Encoders-Decoders General Relativity and Quantum Cosmology Gravitational waves Instrumentation and Detectors Noise reduction Physics Time series black hole coalescence gravitational radiation LIGO binary noise gravitational radiation detector neural network detector non-Gaussianity
ISSN:	2632-2153, 2632-2153
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Abstract	The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.
AbstractList	Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ("glitches"), and its fidelity is tested on real gravitational-wave events from O1 and O2 LIGO-Virgo observing runs. The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.
Author	Bejger, Michał Trovato, Agata Bacon, Philippe
Author_xml	– sequence: 1 givenname: Philippe orcidid: 0000-0003-1350-2037 surname: Bacon fullname: Bacon, Philippe organization: Université Paris Cité, CNRS, Astroparticule et Cosmologie , F-75013 Paris, France – sequence: 2 givenname: Agata surname: Trovato fullname: Trovato, Agata organization: INFN, Sezione di Trieste , I-34127 Trieste, Italy – sequence: 3 givenname: Michał orcidid: 0000-0002-4991-8213 surname: Bejger fullname: Bejger, Michał organization: Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences , Bartycka 18, 00-716 Warszawa, Poland
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Snippet	The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated... Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local...
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StartPage	35024
SubjectTerms	Algorithms Artificial neural networks Black holes Broadband Coders convolutional neural network data analysis Data transmission Datasets denoising autoencoder Encoders-Decoders General Relativity and Quantum Cosmology Gravitational waves Instrumentation and Detectors Noise reduction Physics Time series
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Title	Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder
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Volume	4
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