BCDDM: Branch Correction Denoising Diffusion Model for Black Hole Image Generation

Abstract The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope data to simulated images generated through general relativistic ray tracing (GRRT). However, due to the computationally intensive nature of GRRT, the efficiency of generating specific radiation flux images needs to be improved. This paper introduces the Branch Correction Denoising Diffusion Model (BCDDM), a deep learning framework that synthesizes black hole images directly from physical parameters. The model incorporates a branch correction mechanism and a weighted mixed-loss function to enhance accuracy and stability. We have constructed a data set of 2157 GRRT-simulated images for training the BCDDM, which spans seven key physical parameters of the radiatively inefficient accretion flow model. Our experiments show a strong correlation between the generated images and their physical parameters. By enhancing the GRRT data set with BCDDM-generated images and using ResNet50 for parameter regression, we achieve significant improvements in parameter prediction performance. BCDDM offers a novel approach to reducing the computational costs of black hole image generation, providing a faster and more efficient pathway for data set augmentation, parameter estimation, and model fitting.