A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
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A nonlinear ICA method is derived to estimate general quadratic noise coupling and tested on simulated data plus real KAGRA gravitational wave strain.
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
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Nonlinear Independent Component Analysis Scheme and its application to gravitational wave data analysis
A nonlinear ICA method is derived to estimate general quadratic noise coupling and tested on simulated data plus real KAGRA gravitational wave strain.