Related papers: Bayesian parameter estimation for the Core-bounce …
We perform two-dimensional numerical simulations on the core-collapse of a massive star with strong magnetic fields and differential rotations using a numerical code ZEUS-2D. Changing field configurations and laws of differential rotation…
Precessing black hole-neutron star (BH-NS) binaries produce a rich gravitational wave signal, encoding the binary's nature and inspiral kinematics. Using the lalinference\_mcmc Markov-chain Monte Carlo parameter estimation code, we use two…
We present a fast Bayesian inference framework to address the growing computational cost of gravitational-wave parameter estimation. The increased cost is driven by improved broadband detector sensitivity, particularly at low frequencies…
Though the neutrino-driven convection model for the core-collapse explosion mechanism has received strong support in recent years, there are still many uncertainties in the explosion parameters -- such as explosion energy, remnant mass, and…
In this paper, we present the results of our three-dimensional, multi-group, multi-neutrino-species radiation/hydrodynamic simulation using the state-of-the-art code F{\sc{ornax}} of the terminal dynamics of the core of a non-rotating…
We improve the effective-one-body (EOB) description of nonspinning coalescing black hole binaries by incorporating several recent analytical advances, notably: (i) logarithmic contributions to the conservative dynamics; (ii) resummed…
Phase transitions during inflation can generate a stochastic gravitational-wave background that probes primordial physics. We study the detectability and parameter reconstruction of such a signal with a space-based gravitational-wave…
We present the gravitational wave predictions from two-dimensional core collapse supernova (CCSN) simulations initiated from two nearly identical progenitors that have significantly different internal structures due to their late-stage…
Predictions of the mean and covariance matrix of summary statistics are critical for confronting cosmological theories with observations, not least for likelihood approximations and parameter inference. The price to pay for accurate…
The core rotation rates of massive stars have a substantial impact on the nature of core-collapse supernovae and their compact remnants. We demonstrate that internal gravity waves (IGW), excited via envelope convection during a red…
We propose a novel Bayesian wavelet regression approach using a three-component spike-and-slab prior for wavelet coefficients, combining a point mass at zero, a moment (MOM) prior, and an inverse moment (IMOM) prior. This flexible prior…
We perform general relativistic simulations of stellar core collapse to a proto-neutron star, using a microphysical equation of state as well as an approximate description of deleptonization. We show that for a wide variety of rotation…
Gravitational wave signals from core-collapse supernovae are one of the important observables for extracting the information of dense matter. To extract the properties of proto-neutron stars produced via core-collapse supernovae by…
Using the numerical simulation data for two-dimensional core-collapse supernova, we examine the protoneutron star (PNS) asteroseismology with the relativistic Cowling approximation. As shown in the previous study, the gravitational wave…
Pre-main sequence (PMS) models provide invaluable tools for the study of star forming regions as they allow to assign masses and ages to young stars. Thus it is of primary importance to test the models against observations of PMS stars with…
Gaussian process regression is a widespread tool used to mitigate stellar correlated noise in radial velocity time series. It is particularly useful to search for and determine the properties of signals induced by small-size, low-mass…
Gravitational wave (GW) detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe $\mathcal{O}(100)$s of transient GW events per year. The current methods used to estimate their…
The PE for GW merger events relies on a waveform model calibrated using numerical simulations. Within the Bayesian framework, this waveform model represents the GW signal produced during the merger and is crucial for estimating the…
Gravitational waves from binary neutron star (BNS) mergers can constrain nuclear matter models predicting the neutron star's equation of state (EOS). Matter effects on the inspiral-merger signal are encoded in the multipolar tidal…
The detection and characterization of post-merger gravitational wave signals from binary neutron star mergers remains challenging with current ground-based detectors. We present a convolutional neural network framework designed for…