Deep learning searches for gravitational wave stochastic backgrounds
Deep learning searches for gravitational wave stochastic backgrounds
The background of gravitational waves (GW) has long been studied and remains one of the most exciting aspects in the observation and analysis of gravitational radiation. The paper focuses on the search for the background of gravitational waves using deep neural networks. An astrophysical background due to the presence of many binary black hole coalescences was simulated for Advanced LIGO O3 sensitivity and the Einstein Telescope (ET) design sensitivity. The detection pipeline targets signal data out of the noisy detector background. Its architecture comprises of simulated whitened data as input to three classes of deep neural networks algorithms: a 1D and a 2D convolutional neural network (CNN) and a Long Short Term Memory (LSTM) network. It was found that all three algorithms could distinguish signals from noise with high precision for the ET sensitivity, but the current sensitivity of LIGO is too low to permit the algorithms to learn signal features from the input vectors.
- Nvidia United States
- Scuola Normale Superiore Italy
- IE University Spain
- Laboratoire d'Annecy-le-Vieux de physique des particules France
- Institut National de Physique Nucléaire et de Physique des Particules France
LIGO: sensitivity, gravitational radiation: stochastic, deep learning searches, deep neural networks algorithms, Sensitivity, Einstein Telescope, LIGO, [PHYS.GRQC] Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Detectors, artificial intelligence, neural nets, gravitational waves, simulated whitened data, black hole: coalescence, Long Short Term Memory network, LSTM, Neural networks, CNN, gravitational wave stochastic backgrounds, 2D convolutional neural network, noise, data analysis method, neural network, black hole: binary: coalescence, binary black hole coalescences, ET sensitivity, Time series analysis, Gravitational waves, exciting aspects, gravitational wave detectors, Deep Learning, Stochastic processes, Gravitational Wave Backgrounds, Advanced LIGO O3 sensitivity, noisy detector background, CNN; Deep Learning; ET; Gravitational Wave Backgrounds; LIGO; LSTM, gravitational radiation: background, gravitational radiation, Einstein Telescope design sensitivity, Deep learning, astrophysical background, black holes, background: stochastic, gravitational radiation detector, detection pipeline targets, black hole: binary, network, learning (artificial intelligence), ET
LIGO: sensitivity, gravitational radiation: stochastic, deep learning searches, deep neural networks algorithms, Sensitivity, Einstein Telescope, LIGO, [PHYS.GRQC] Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Detectors, artificial intelligence, neural nets, gravitational waves, simulated whitened data, black hole: coalescence, Long Short Term Memory network, LSTM, Neural networks, CNN, gravitational wave stochastic backgrounds, 2D convolutional neural network, noise, data analysis method, neural network, black hole: binary: coalescence, binary black hole coalescences, ET sensitivity, Time series analysis, Gravitational waves, exciting aspects, gravitational wave detectors, Deep Learning, Stochastic processes, Gravitational Wave Backgrounds, Advanced LIGO O3 sensitivity, noisy detector background, CNN; Deep Learning; ET; Gravitational Wave Backgrounds; LIGO; LSTM, gravitational radiation: background, gravitational radiation, Einstein Telescope design sensitivity, Deep learning, astrophysical background, black holes, background: stochastic, gravitational radiation detector, detection pipeline targets, black hole: binary, network, learning (artificial intelligence), ET
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