相关论文: Deep-Learning Classification and Parameter Inferen…
Recent core-collapse supernova (CCSN) simulations have predicted several distinct features in gravitational-wave (GW) spectrograms, including a ramp-up signature due to the g-mode oscillation of the proto-neutron star (PNS) and an excess in…
Gravitational waves from core-collapse supernovae offer a unique probe of the equation of state (EOS) of dense nuclear matter. For rapidly rotating stars, previous machine-learning studies demonstrated promising EOS classification accuracy.…
We study the properties of the gravitational wave (GW) emission between $10^{-5}$ Hz and $50$ Hz (which we refer to as low-frequency emission) from core-collapse supernovae, in the context of studying such signals in laser interferometric…
The gravitational wave (GW) signal resulting from stellar core collapse encodes a wealth of information about the physical parameters of the progenitor star and the resulting core-collapse supernova (CCSN). We present a novel approach to…
The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for…
Abridged: Using a suite of progenitor models, neutrino luminosities, and two- dimensional (2D) simulations, we investigate the matter gravitational-wave (GW) emission from postbounce phases of neutrino-driven core-collapse supernovae…
Gravitational waves (GWs) can provide crucial information about the central engines of core-collapse supernovae (CCSNe). In order to unveil the nature of GW emission in CCSNe, we apply perturbative analyses with the same underlying…
In this work we report briefly on the gravitational wave (GW) signal computed in the context of a self-consistent, 3D simulation of a core-collapse supernova (CCSN) explosion of a 15M$_\odot$ progenitor star. We present a short overview of…
Modeling core-collapse supernovae (CCSNe) with neutrino transport in three dimensions (3D) requires tremendous computing resources and some level of approximation. We present a first comparison study of CCSNe in 3D with different physics…
The ensemble of unresolved compact binary coalescences is a promising source of the stochastic gravitational wave (GW) background. For stellar-mass black hole binaries, the astrophysical stochastic GW background is expected to exhibit…
Understanding gravitational wave emission from core-collapse supernovae will be essential for their detection with current and future gravitational wave detectors. This requires a sample of waveforms from modern 3D supernova simulations…
We present self-consistent three-dimensional core-collapse supernova simulations of a rotating $20M_\odot$ progenitor model with various initial angular velocities from $0.0$ to $4.0$ rad s$^{-1}$ using a smoothed particle hydrodynamics…
The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space…
We present a detailed theoretical analysis of the gravitational-wave (GW) signal of the post-bounce evolution of core-collapse supernovae (SNe), employing for the first time relativistic, two-dimensional (2D) explosion models with…
The quantitative impact of strong rotation on the amplitudes and frequencies of the post-bounce gravitational wave (GW) signal from core-collapse supernovae (CCSNe) is still not fully understood. To study trends in frequencies and…
A detection of a core-collapse supernova signal with an Advanced LIGO and Virgo gravitational-wave detector network will allow us to measure astrophysical parameters of the source. In real advanced gravitational-wave detector data there are…
Gravitational waves from core-collapse supernovae are a promising yet challenging target for detection due to the stochastic and complex nature of these signals. Conventional detection methods for core-collapse supernovae rely on excess…
Recent multi-dimensional (multi-D) core-collapse supernova (CCSN) simulations characterize gravitational waves (GWs) and neutrino signals, offering insight into universal properties of CCSN independent of progenitor. Neutrino analysis in…
In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived from three-dimensional numerical simulations of waveforms,…
In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has…