📝 SummarXiv

Abstract

Recent imaging of supermassive black holes by the Event Horizon Telescope (EHT) has relied on exhaustive parameter-space searches, matching observations to large, precomputed libraries of theoretical models. As observational data become increasingly precise, the limitations of this computationally expensive approach grow more acute, creating a pressing need for more efficient methods. In this work, we present $\texttt{Jipole}$, an automatically differentiable (AD), $\texttt{ipole}$-based code for radiative transfer in curved spacetimes, designed to compute image gradients with respect to underlying model parameters. These gradients quantify how parameter changes-such as the black hole's spin or the observer's inclination-affect the image, enabling more efficient parameter estimation and reducing the number of required images. We validate $\texttt{Jipole}$ against $\texttt{ipole}$ in two analytical tests and then compare pixel-wise intensity derivatives from AD with those from finite-difference methods. We then demonstrate the utility of these gradients by performing parameter recovery for an analytical model using a conjugate gradient optimizer in three increasingly complex cases for the injected image: ideal, blurred, and blurred with added noise. In most cases, high-accuracy fits are obtained in only a few optimization steps, failing only in cases with extremely low signal-to-noise ratios and large blurring size kernels. These results highlight the potential of AD-based methods to accelerate robust, high-fidelity model-data comparisons in current and future black hole imaging efforts.