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.
The gravitational wave signal from core-collapse supernovae
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We study gravitational waves (GWs) from a set of two-dimensional multi-group neutrino radiation hydrodynamic simulations of core-collapse supernovae (CCSNe). Our goal is to systematize the current knowledge about the post-bounce CCSN GW signal and recognize the templatable features that could be used by the ground-based laser interferometers. We demonstrate that starting from ~400ms after core bounce the dominant GW signal represents the fundamental quadrupole (l=2) oscillation mode (f-mode) of the proto-neutron star (PNS), which can be accurately reproduced by a linear perturbation analysis of the angle-averaged PNS profile. Before that, in the time interval between ~200 and ~400ms after bounce, the dominant mode has two radial nodes and represents a g-mode. We associate the high-frequency noise in the GW spectrograms above the main signal with p-modes, while below the dominant frequency there is a region with very little power. The collection of models presented here summarizes the dependence of the CCSN GW signal on the progenitor mass, equation of state, many-body corrections to the neutrino opacity, and rotation. Weak dependence of the dominant GW frequency on the progenitor mass motivates us to provide a simple fit for it as a function of time, which can be used as a prior when looking for CCSN candidates in the LIGO data.
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.
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.
-
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.