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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
gr-qc 2years
2026 2verdicts
UNVERDICTED 2roles
background 2polarities
background 2representative citing papers
Basilic is an end-to-end Bayesian pipeline for gravitational-wave burst inference and model classification, with a case study showing signal degeneracies between binary black hole mergers and cosmic strings.
citing papers explorer
-
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.
-
Basilic: An end-to-end pipeline for Bayesian burst inference and model classification in gravitational-wave data
Basilic is an end-to-end Bayesian pipeline for gravitational-wave burst inference and model classification, with a case study showing signal degeneracies between binary black hole mergers and cosmic strings.