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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Vydané v:Machine learning: science and technology Ročník 4; číslo 3; s. 35024 - 35044
Hlavní autori: Bacon, Philippe, Trovato, Agata, Bejger, Michał
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.09.2023
IOP Publishing Ltd
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:MLST-100943.R1
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2632-2153
2632-2153
DOI:10.1088/2632-2153/acd90f