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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Simulations of unlensed binary black hole mergers show that ~0.01% of event pairs are falsely classified as lensed by GLANCE at SNR threshold 1.5 with time delays of ~1000 days or more.
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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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False Alarm Rates in Detecting Gravitational Wave Lensing from Astrophysical Coincidences: Insights with Model-Independent Technique GLANCE
Simulations of unlensed binary black hole mergers show that ~0.01% of event pairs are falsely classified as lensed by GLANCE at SNR threshold 1.5 with time delays of ~1000 days or more.
- Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors