用深度学习加速黑洞图像生成，提升参数推断效率
BCDDM: Branch-Corrected Denoising Diffusion Model for Black Hole Image Generation
- 引入分支修正扩散模型，直接从物理参数生成黑洞图像
- 生成图像与物理参数高度相关，提升参数预测准确率
- 适合天体物理、图像生成和计算优化方向的研究者
通过广义相对论光线追踪（GRRT）模拟图像并拟合事件视界望远镜（EHT）数据，可推断黑洞及吸积流特性。然而GRRT计算成本高昂，图像生成效率亟待提升。本文提出分支修正去噪扩散模型（BCDDM），直接从辐射不显著吸积流（RIAF）模型的七种关键物理参数生成黑洞图像。模型采用分支修正机制与加权混合损失函数，增强生成精度与稳定性。我们构建了2,157张基于GRRT的训练图像数据集。实验表明生成图像与物理参数间存在强相关性。通过将BCDDM生成图像扩充至GRRT数据集，并使用ResNet50进行参数回归，显著提升了参数估计性能。该方法为降低黑洞图像生成计算开销提供了新路径，实现更高效的数据库扩充、参数估计与模型拟合。
原文摘要 · Abstract (English)
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.
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