Parameter Inference from Black Hole Images using Deep Learning in Visibility Space

Using very long baseline interferometry, the Event Horizon Telescope (EHT) collaboration has resolved the shadows of two supermassive black holes. Model comparison is traditionally performed in image space, where imaging algorithms introduce uncertainties in the recovered structure. Here, we develop a deep learning framework to infer the physical parameters of General Relativistic Magnetohydrodynamic (GRMHD) simulations directly from visibility space data. By working in the native domain of the interferometer, our method avoids introducing potential errors and biases from image reconstruction. First, we train and validate our framework on synthetic data derived from GRMHD simulations that vary in magnetic field state, spin, and R_high. Applying these models to the real data obtained during the 2017 EHT campaign, and only considering total intensity, we do not derive meaningful constraints on either spin or R_high. At present, our method is limited both by theoretical uncertainties in the GRMHD simulations and variations between snapshots of the same underlying physical model. However, we demonstrate that spin and R_high could be recovered using this framework through continuous monitoring of our sources, which mitigates variations due to turbulence. In future work, we anticipate that including spectral or polarimetric information will greatly improve the performance of this framework.