BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation

TL;DR

This paper presents BCDDM, a deep learning model that efficiently generates black hole images from physical parameters, reducing computational costs and improving parameter estimation accuracy.

Contribution

The paper introduces BCDDM, a novel branch correction denoising diffusion model that synthesizes black hole images directly from physical parameters, enhancing efficiency and accuracy.

Findings

Strong correlation between generated images and physical parameters.

Significant improvement in parameter prediction performance.

Effective dataset augmentation with generated images.

Abstract

The properties of black holes and accretion flows can be inferred by fitting Event Horizon Telescope (EHT) 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 dataset of 2,157 GRRT-simulated images for training the BCDDM, which spans seven key physical parameters of the radiatively inefficient accretion flow (RIAF) model. Our experiments show a strong correlation between the generated images and their physical parameters. By enhancing the GRRT dataset 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 dataset augmentation, parameter estimation, and model fitting.