Bayesian techniques for comparing time-dependent GRMHD simulations to variable Event Horizon Telescope observations

TL;DR

This paper introduces a Bayesian framework for comparing time-dependent GRMHD simulations with variable EHT observations of black holes, improving parameter inference and model selection by accounting for variability.

Contribution

It develops a novel Bayesian method that explicitly incorporates variability in both data and models for comparing EHT observations with GRMHD simulations.

Findings

Time-independent models cause biased parameter estimates.

Neglecting variability leads to incorrect model choices.

The method successfully applied to early Sgr A* EHT data.

Abstract

The Event Horizon Telescope (EHT) is a millimeter-wavelength, very-long-baseline interferometry (VLBI) experiment that is capable of observing black holes with horizon-scale resolution. Early observations have revealed variable horizon-scale emission in the Galactic Center black hole, Sagittarius A* (Sgr A*). Comparing such observations to time-dependent general relativistic magnetohydrodynamic (GRMHD) simulations requires statistical tools that explicitly consider the variability in both the data and the models. We develop here a Bayesian method to compare time-resolved simulation images to variable VLBI data, in order to infer model parameters and perform model comparisons. We use mock EHT data based on GRMHD simulations to explore the robustness of this Bayesian method and contrast it to approaches that do not consider the effects of variability. We find that time-independent models lead to offset values of the inferred parameters with artificially reduced uncertainties. Moreover, neglecting the variability in the data and the models often leads to erroneous model selections. We finally apply our method to the early EHT data on Sgr A*.