Bayesian Black Hole Photogrammetry

TL;DR: Researchers propose a Bayesian model for black hole imaging, accurately reproducing simulated images and recovering black hole properties from Event Horizon Telescope data, highlighting the importance of Bayesian inference in resolving image degeneracies and estimating black hole parameters.

Abstract: Abstract We propose an analytic dual-cone accretion model for horizon-scale images of the cores of low-luminosity active galactic nuclei, including those observed by the Event Horizon Telescope (EHT). Our model is of synchrotron emission from an axisymmetric, magnetized plasma, constrained to flow within two oppositely oriented cones that are aligned with the black hole’s spin axis. We show this model can accurately reproduce images of a variety of time-averaged general relativistic magnetohydrodynamic simulations and that it accurately recovers the black hole spin, orientation, emission scale height, peak emission radius, and fluid flow direction from these simulations within a Bayesian inference framework using radio interferometric data. We show that nontrivial topologies in the images of relativistic accretion flows around black holes can result in nontrivial multimodal solutions when applied to observations with a sparse array, such as the EHT 2017 observations of M87*. The presence of these degeneracies underscores the importance of employing Bayesian techniques to adequately sample the posterior space for the interpretation of EHT measurements. We fit our model to the EHT observations of M87* and find a 95% highest posterior density interval for the mass-to-distance ratio of θ g ∈ (2.84, 3.75) μ as, and give an inclination of θ o ∈ (11°, 24°). These new measurements are consistent with mass measurements from the EHT and stellar dynamical estimates and with the spin axis inclination inferred from properties of the M87* jet.