Papers › Bayesian Black Hole Photogrammetry
Bayesian Black Hole Photogrammetry
Dominic O. Chang, Michael D. Johnson, Paul Tiede, Daniel C. M. Palumbo
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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 solutions when applied to observations from a sparse array, such as the EHT 2017 observations of M87${}^*. The presence of these degeneracies underscores the importance of employing Bayesian techniques that adequately sample the posterior space for the interpretation of EHT measurements. We fit our model to the EHT observations of M87{}^*$ and find a 95% Highest Posterior Density Interval (HPDI) for the mass-to-distance ratio of $\theta_g\in(2.84,3.75)\,\mu{\rm as}$, and give an inclination of $\theta_{\rm o}\in(11^\circ,24^\circ). These new measurements are consistent with mass measurements from the EHT and stellar dynamical estimates (e.g., Gebhardt et al. 2011; EHTC et al. 2019a,b; Liepold et al. 2023), and with the spin axis inclination inferred from properties of the M87{}^*$ jet (e.g., Walker et al. 2018).
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