Sensitivities of Black Hole Images from GRMHD Simulations

TL;DR

This paper introduces a differentiable radiative transfer method using GRMHD simulations to analyze black hole images, revealing complex error landscapes and demonstrating the effectiveness of gradient-based parameter recovery.

Contribution

It presents a novel approach to compute pixel-wise image sensitivities from GRMHD simulations, enabling gradient-based analysis and improved parameter inference in black hole imaging.

Findings

Structured error landscape with anisotropies and local minima affects parameter fitting.

Gradient-based methods can effectively recover parameters even with noise.

Automatic differentiation facilitates high-precision model-data comparisons.

Abstract

The advent of high-fidelity imaging of supermassive black holes calls for efficient and robust data-analysis methods. In this work, we use $\texttt{Jipole}$, a differentiable, $\texttt{ipole}$-based radiative transfer code, to enable gradient-based analyses of images generated from state-of-the-art general relativistic magnetohydrodynamic (GRMHD) simulations. We compute image sensitivities, i.e., pixel-wise derivatives of the intensity with respect to model parameters, which form the Jacobian of the forward model and define a local map from parameter space to image space. Using these sensitivities in a mock data analysis, we find that GRMHD-based images generate a structured error landscape for parameter fitting, with anisotropies and local minima, making parameter exploration nontrivial but still tractable when guided by gradient information. We characterize this landscape through the Jacobian and assess the feasibility of gradient-based recovery under idealized, blurred, and noisy conditions. Our results show that automatic differentiation-computed image gradients can guide parameter exploration effectively even in the presence of noise. These findings establish a basis for efficient, high-precision model--data comparisons in black hole imaging and motivate the integration of these sensitivities into advanced inference frameworks.