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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Ratio-Filter Dechirping converts gravitational-wave matched filtering from a memory-bound FFT into a cache-efficient FIR convolution, delivering a measured 8x speedup in the core loop.
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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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Beyond FINDCHIRP: Breaking the memory wall and optimal FFTs for Gravitational-Wave Matched-Filter Searches with Ratio-Filter Dechirping
Ratio-Filter Dechirping converts gravitational-wave matched filtering from a memory-bound FFT into a cache-efficient FIR convolution, delivering a measured 8x speedup in the core loop.