通过潜在空间扩散模型加速黑洞图像生成
Accelerating Black Hole Image Generation via Latent Space Diffusion Models
AI总结：
本文提出一种基于潜在空间的扩散模型，通过物理条件生成高保真黑洞图像，显著提升生成效率和图像质量，减少计算成本四倍以上。
摘要：
目前解释视界尺度黑洞图像依赖于计算密集型广义相对论射线追踪（GRRT）模拟，这在快速参数探索和强场引力高精度检验中构成了重大瓶颈。我们证明，由磁化吸积流合成的物理准确的黑洞图像本质上存在于一个低维流形上，编码时空几何、等离子体分布和相对论发射的特征。利用这一结构，我们引入了一种物理条件的扩散模型，该模型在紧凑的潜在空间中运行，直接从物理参数生成高保真的黑洞图像。该模型能够准确再现完整GRRT模拟的关键观测特征，如阴影直径、光环结构和相对论亮度不对称性，同时计算成本降低了四倍以上。与之前的去噪扩散模型相比，所提出的方法在图像质量、重建保真度和参数估计准确性方面实现了显著改进，同时将每个黑洞图像的平均推断时间从5.25秒减少到1.15秒。我们的工作确立了扩散基于的潜在模型作为传统辐射传输求解器的高效可扩展替代品，为下一代黑洞成像的实时建模和推断提供了实用框架。
英文摘要：
Interpreting horizon-scale black hole images currently relies on computationally intensive General Relativistic Ray Tracing (GRRT) simulations, which pose a significant bottleneck for rapid parameter exploration and high-precision tests of strong-field gravity. We demonstrate that physically accurate black hole images, synthesized from magnetized accretion flows, inherently reside on a low-dimensional manifold-encoding the essential features of spacetime geometry, plasma distribution, and relativistic emission. Leveraging this structure, we introduce a physics-conditioned diffusion model that operates in a compact latent space to generate high-fidelity black hole imagery directly from physical parameters. The model accurately reproduces critical observational signatures from full GRRT simulations-such as shadow diameter, photon-ring structure, and relativistic brightness asymmetry-while achieving over a fourfold reduction in computational expense. Compared with the previous generation of denoising diffusion models, the proposed approach achieves significant improvements in image quality, reconstruction fidelity, and parameter estimation accuracy, while reducing the average inference time per black hole image from 5.25 seconds to 1.15 seconds. Our work establishes diffusion-based latent models as efficient and scalable substitutes for traditional radiative transfer solvers, offering a practical framework toward real-time modeling and inference for next-generation black hole imaging.
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