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Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering.
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2019
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2020
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P. G. Krastev, “Real-time detection of gravitational waves from binary neutron stars using artificial neural networks,” Physics Letters B
2020
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2020
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B.-J. Lin, X.-R. Li, and W.-L. Yu, “Binary neutron stars gravitational wave detection based on wavelet packet analysis and convolutional neural networks,” Frontiers of Physics
2020
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A. J. Chua and M. Vallisneri, “Learning bayesian posteriors with neural networks for gravitational-wave inference,” Physical Review Letters
2020
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2020
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W. Wei and E. Huerta, “Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning,” Physics Letters B
2020
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A. Khan, E. Huerta, and A. Das, “Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers,” Physics Letters B
2020
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2020
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