Bayesian Black Hole Photogrammetry
Dominic O. Chang, Michael D. Johnson, Paul Tiede, and Daniel C. M. Palumbo

TL;DR
This paper introduces a simple, analytic dual-cone accretion model for black hole images that accurately reproduces observational data, enabling Bayesian inference of black hole parameters from radio interferometry.
Contribution
The paper presents a novel, efficient dual-cone accretion model that accurately fits horizon-scale images and allows Bayesian parameter estimation from EHT data.
Findings
Model accurately reproduces GRMHD simulation images.
Successfully recovers black hole and emission parameters.
Provides consistent mass and inclination estimates for M87*.
Abstract
We propose a simple, analytic dual-cone accretion model for horizon scale images of the cores of Low-Luminosity Active Galactic Nuclei (LLAGN), including those observed by the Event Horizon Telescope (EHT). Our underlying model is of synchrotron emission from an axisymmetric, magnetized plasma, which is constrained to flow within two oppositely oriented cones that are aligned with the black hole's spin axis. We show that this model can accurately reproduce images for a variety of time-averaged general relativistic magnetohydrodynamic (GRMHD) simulations, that it accurately recovers both the black hole and emission parameters from these simulations, and that it is sufficiently efficient to be used to measure these parameters in a Bayesian inference framework with radio interferometric data. We show that non-trivial topologies in the source image can result in non-trivial multi-modal…
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Taxonomy
TopicsStatistical and numerical algorithms · Advanced Vision and Imaging
