GALLIFRAY -- A geometric modeling and parameter estimation framework for black hole images using bayesian techniques

TL;DR

GALLIFRAY is an open-source Python framework designed for parameter estimation of black hole images from VLBI data, emphasizing modularity, efficiency, and adaptability, demonstrated through simulated data fitting.

Contribution

It introduces a flexible, modular framework for black hole image modeling and parameter estimation using Bayesian techniques with VLBI data.

Findings

Successful convergence of posterior distributions in simulated data tests

Framework's modular design facilitates diverse modeling approaches

Demonstrated effectiveness with geometric and physical models

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). Being 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 \texttt{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 \texttt{GALLIFRAY}. As an illustration, we fit a geometric and a physical model to simulated datasets 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.