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GALLIFRAY -- A geometric modeling and parameter estimation framework for black hole images using bayesian techniques
Saurabh, Sourabh Nampalliwar
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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.
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