GALLIFRAY—A Geometric Modeling and Parameter Estimation Framework for Black Hole Images Using Bayesian Techniques

Abstract

Abstract Recent observations of the galactic centers of M87 and the Milky Way with the Event Horizon Telescope have ushered in a new era of black-hole-based tests of fundamental physics using very long baseline interferometry (VLBI). Since it is a nascent field, there are several different modeling and analysis approaches in vogue (e.g., geometric and physical models, visibility and closure amplitudes, agnostic and multimessenger priors). We present GALLIFRAY, an open-source, Python-based framework for estimation/extraction of parameters using VLBI data. It is developed with modularity, efficiency, and adaptability as the primary objectives. This article outlines the design and usage of GALLIFRAY. As an illustration, we fit a geometric and a physical model to simulated data sets using Markov Chain Monte Carlo sampling and find good convergence of the posterior distribution. We conclude with an outline of further enhancements currently in development.