相关论文: Variational Autoencoder with Normalizing flow for …

Semi-analytical waveform models for black hole binaries require calibration against numerical relativity waveforms to accurately represent the late inspiral and merger, where analytical approximations fail. After the fitting coefficients…

While supermassive black-hole (SMBH)-binaries are not the only viable source for the low-frequency gravitational wave background (GWB) signal evidenced by the most recent pulsar timing array (PTA) data sets, they are expected to be the most…

Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Commonly, latent variables are modeled using the normal…

We investigate spontaneous vectorization in the Einstein-Maxwell-Vector (EMV) model, introducing a novel mechanism driven by the interplay between electromagnetic and vector fields. A key innovation in our work is the resolution of an…

Data derived from general relativistic magnetohydrodynamic simulations of accretion onto black holes can be used as input to a postprocessing scheme that predicts the radiated spectrum. Combining a relativistic Compton scattering radiation…

Normalising-flow variational inference (VI) can approximate complex posteriors, yet single-flow models often behave inconsistently across qualitatively different distributions. We propose Adaptive Mixture Flow Variational Inference…

We present a novel spectral solver for general relativistic magnetohydrodynamics on dynamical spacetimes. By combining a high order discontinuous spectral method on mapped Chebyshev Fourier grids, our scheme attains exponential convergence.…

We investigate accretion disk winds commonly observed in galactic black hole (BH) X-ray binaries (XRB), which manifest as blueshifted absorption features in X-ray spectra. We model these winds as ideal magnetohydrodynamic outflows of hot…

Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This chapter provides a unified framework to handle these approaches via Markov chains. We consider stochastic normalizing flows as…

We present an overview of recent numerical advances in the theoretical characterization of massive binary black hole (MBBH) mergers in astrophysical environments. These systems are among the loudest sources of gravitational waves (GWs) in…

This is an unconventional review article on spectral problems in black hole perturbation theory. Our purpose is to explain how to apply various known techniques in quantum mechanics to such spectral problems. The article includes…

In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state…

Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predominantly rely on using a large number of posterior samples…

We develop a novel deep learning method for uncertainty quantification in stochastic partial differential equations based on Bayesian neural network (BNN) and Hamiltonian Monte Carlo (HMC). A BNN efficiently learns the posterior…

A model of low-frequency quasi-periodic oscillations (LFQPOs) of black hole X-ray binaries (BHXBs) is proposed based on the perturbed magnetohydrodynamic (MHD) equations of accretion disk. It turns out that the LFQPOs frequencies of some…

We present techniques for successfully performing numerical relativity simulations of binary black holes with fourth-order accuracy. Our simulations are based on a new coding framework which currently supports higher order finite…

In this paper, we expand upon previous work that argued for the possibility of a sub-equipartition magnetic field in the accretion flow of a black hole binary system. Using X-ray observations of the three well-known sources A0620-00, XTE…

We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximation of maximum likelihood channel estimation.…

Very long baseline interferometry (VLBI) at millimeter (mm) wavelengths is being employed to resolve event-horizon scale structure of the environment surrounding the Milky-Way black hole, at an angular resolution of a few tens of…

We investigate state transitions in black hole X-ray binaries through different parameters by using two-dimensional axisymmetric hydrodynamical simulation method. For radiative cooling in hot accretion flow, we take into account the…