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.
Multiple physical elements to determine the gravitational-wave signatures of core-collapse supernovae
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We review recent progress in the theoretical predictions of gravitational waves (GWs) of core-collapse supernovae. Following a brief summary of the methods in the numerical modeling, we summarize multiple physical elements that determine the GW signatures which have been considered to be important in extracting the information of the long-veiled explosion mechanism from the observation of the GWs. We conclude with a summary of the most urgent tasks to make the dream come true.
citation-role summary
background 1
citation-polarity summary
fields
gr-qc 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
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.