相关论文: Fast and accurate prediction of numerical relativi…

We revisit the scenario of small-mass-ratio (q) black-hole binaries; performing new, more accurate, simulations of mass ratios 10:1 and 100:1 for initially nonspinning black holes. We propose fitting functions for the trajectories of the…

Coalescing binary black holes are among the primary science targets for second generation ground-based gravitational wave (GW) detectors. Reliable GW models are central to detection of such systems and subsequent parameter estimation. This…

The paper addresses Bayesian inferences in inverse problems with uncertainty quantification involving a computationally expensive forward map associated with solving a partial differential equations. To mitigate the computational cost, the…

In this study of the `Resolving supermAssive Black hole Binaries In galacTic hydrodynamical Simulations' (RABBITS) series, we focus on the hardening and coalescing process of supermassive black hole (SMBH) binaries in galaxy mergers. For…

Observation and characterisation of gravitational waves from binary black holes requires accurate knowledge of the expected waveforms. The late inspiral and merger phase of the waveform is obtained through direct numerical integration of…

We present the first results from our Post-Newtonian (PN) Smoothed Particle Hydrodynamics (SPH) code, which has been used to study the coalescence of binary neutron star (NS) systems. The Lagrangian particle-based code incorporates…

Gravitational wave observations from merging compact objects are becoming commonplace, and as detectors improve and gravitational wave sources become more varied, it is increasingly important to have dense and expansive template banks of…

We present several high-accuracy surrogate models for gravitational-wave signals from equal-mass head-on mergers of Proca stars, computed through the Newman-Penrose scalar $\psi_4$. We also discuss the current state of the model extensions…

We present a comprehensive comparison between numerical relativity (NR) angular momentum fluxes at infinity and the corresponding quantity entering the radiation reaction in TEOBResumS, an Effective-One-Body (EOB) waveform model for…

The plunge-merger stage of the binary-black hole coalescence, when the bodies' velocities reach a large fraction of the speed of light and the gravitational-wave luminosity peaks, provides a unique opportunity to probe gravity in the…

Fast machine learning-based surrogate models are trained to emulate slow, high-fidelity engineering simulation models to accelerate engineering design tasks. This introduces uncertainty as the surrogate is only an approximation of the…

Detecting binary black holes in interferometer data requires an accurate knowledge of the orbital phase evolution of the system. From the point of view of data analysis one also needs fast algorithms to compute the templates that will…

We present a new phenomenological gravitational waveform model for the inspiral and coalescence of non-precessing spinning black hole binaries. Our approach is based on a frequency domain matching of post-Newtonian inspiral waveforms with…

Dynamical captures of black holes may take place in dense stellar media due to the emission of gravitational radiation during a close passage. Detection of such events requires detailed modelling, since their phenomenology qualitatively…

Gravitational waveforms for compact binary coalescences (CBCs) have been invaluable for detections by the LIGO-Virgo collaboration. They are obtained by a combination of semi-analytical models and numerical simulations. So far systematic…

Predictive estimation, which comprises model calibration, model prediction, and validation, is a common objective when performing inverse uncertainty quantification (UQ) in diverse scientific applications. These techniques typically require…

Coalescing binary black-hole systems are among the most promising sources of gravitational waves for ground-based interferometers. While the \emph{inspiral} and \emph{ring-down} stages of the binary black-hole coalescence are well-modelled…

We analyze a prospect for predicting gravitational waveforms from compact binaries based on automated machine learning (AutoML) from around a hundred different possible regression models, without having to resort to tedious and manual…

This paper presents a neural network--enhanced surrogate modeling approach for diffusion problems with spatially varying random field coefficients. The method builds on numerical homogenization, which compresses fine-scale coefficients into…

We present the first application of deep learning forecasting for binary neutron stars, neutron star - black hole systems, and binary black hole mergers that span an eccentricity range e <= 0.9. We train neural networks that describe these…