相关论文: Evaluating Machine Learning Models for Supernova G…

By performing a series of two-dimensional, special relativistic magnetohydrodynamic (MHD) simulations, we study signatures of gravitational waves (GWs) in the magnetohydrodynamically-driven core-collapse supernovae. In order to extract the…

Recently exploratory studies were performed on the possibility of constraining the neutron star equation of state (EOS) using signals from coalescing binary neutron stars, or neutron star-black hole systems, as they will be seen in upcoming…

We have done a series of two-dimensional hydrodynamic simulations of the rotational collapse of a supernova core in axisymmetry. We have employed a realistic equation of state (EOS) and taken into account electron captures and neutrino…

We present a Bayesian framework for joint and coherent analyses of multimessenger binary neutron star signals. The method, implemented in our bajes infrastructure, incorporates a joint likelihood for multiple datasets, support for various…

We compare gravitational-wave (GW) signals from eight three-dimensional simulations of core-collapse supernovae, using two different progenitors with zero-age main sequence masses of 9 and 20 solar masses. The collapse of each progenitor…

A detection of a core-collapse supernova (CCSN) gravitational-wave (GW) signal with an Advanced LIGO and Virgo detector network may allow us to measure astrophysical parameters of the dying massive star. GWs are emitted from deep inside the…

This work investigates the problem of detecting gravitational wave (GW) events based on simulated damped sinusoid signals contaminated with white Gaussian noise. It is treated as a classification problem with one class for the interesting…

Models of gravitational waveforms play a critical role in detecting and characterizing the gravitational waves (GWs) from compact binary coalescences. Waveforms from numerical relativity (NR), while highly accurate, are too computationally…

Context. Gravitational waves (GW) provide a unique probe of the explosion mechanism of massive stars and the evolution of nascent proto-neutron stars (PNS). Magnetorotational explosions are one of the promising non-canonical core-collapse…

Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO is limited by the control noises from auxiliary degrees of freedom, which nonlinearly couple to the main GW readout. One particularly promising way…

The field of gravitational wave (GW) detection is progressing rapidly, with several next-generation observatories on the horizon, including LISA. GW data is challenging to analyze due to highly variable signals shaped by source properties…

The eventual detection of gravitational waves from core-collapse supernovae (CCSN) will help improve our current understanding of the explosion mechanism of massive stars. The stochastic nature of the late post-bounce gravitational wave…

Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning…

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of…

The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high…

The frequencies and damping times of the non radial oscillations of neutron stars are computed for a set of recently proposed equations of state (EOS) which describe matter at supranuclear densites. These EOS are obtained within two…

The gravitational-wave (GW) detector data are affected by short-lived instrumental or terrestrial transients, called glitches, which can simulate GW signals. Mitigation of glitches is particularly difficult for algorithms which target…

The direct detection of gravitational waves (GWs) by LIGO has strikingly confirmed general relativity (GR), but testing GR via GWs requires estimating parameterized post-Einsteinian (ppE) deviation parameters in waveform models. Traditional…

In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the…

We seek to achieve the Holy Grail of Bayesian inference for gravitational-wave astronomy: using deep-learning techniques to instantly produce the posterior $p(\theta|D)$ for the source parameters $\theta$, given the detector data $D$. To do…