Testing Bayesian inference of GRMHD model parameters from VLBI data

TL;DR

This paper introduces a Bayesian method to efficiently explore GRMHD model parameters from VLBI data, improving parameter inference accuracy for black hole imaging.

Contribution

We develop an adaptive Bayesian framework integrating GRMHD simulations with VLBI data analysis, enabling continuous parameter space exploration and better handling of variability.

Findings

Successfully recovers parameters from simulated data

Handles time variability with an inflated error model

Enables expansion of model parameter space

Abstract

Recent observations by the Event Horizon Telescope (EHT) of supermassive black holes M87* and Sgr A* offer valuable insights into their spacetime properties and astrophysical conditions. Utilizing a library of model images (~2 million for Sgr A*) generated from general-relativistic magnetohydrodynamic (GRMHD) simulations, limited and coarse insights on key parameters such as black hole spin, magnetic flux, inclination angle, and electron temperature were gained. The image orientation and black hole mass estimates were obtained via a scoring and an approximate rescaling procedure. Lifting such approximations, probing the space of parameters continuously, and extending the parameter space of theoretical models is both desirable and computationally prohibitive with existing methods. To address this, we introduce a new Bayesian scheme that adaptively explores the parameter space of ray-traced, GRMHD models. The general relativistic radiative transfer code \ipole is integrated with the EHT parameter estimation tool THEMIS. The pipeline produces a ray-traced model image from GRMHD data, computes predictions for VLBI observables from the image for a specific VLBI array configuration and compares to data thereby sampling the likelihood surface via an MCMC scheme. At this stage we focus on four parameters: accretion rate, electron thermodynamics, inclination, and source position angle. Our scheme faithfully recovers parameters from simulated VLBI data and accommodates time-variabibility via an inflated error budget. We highlight the impact of intrinsic variability on model fitting approaches. This work facilitates more informed inferences from GRMHD simulations and enables expansion of the model parameter space in a statistically robust and computationally efficient manner.