Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.
Cuoco et al., Mach
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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.
A CNN framework using response functions from gravitational wave mismatches classifies signals as GR or beyond-GR with 33 times better sensitivity than raw waveforms and detects massive gravity deviations at graviton masses around 10^{-23} eV/c².
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.
citing papers explorer
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Predicting intermediate-mass black hole formation in star clusters with machine learning
Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.
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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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Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework
A CNN framework using response functions from gravitational wave mismatches classifies signals as GR or beyond-GR with 33 times better sensitivity than raw waveforms and detects massive gravity deviations at graviton masses around 10^{-23} eV/c².
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Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.