迈向 M87 和银河（Milky Way）中超大质量黑洞（supermassive black hole）的独立事件视界（event horizon）成像

本文旨在通过独立于现有方法的数据分析和图像重建技术，对 M87* 和 Sgr A* 的公开数据集进行研究。研究团队利用闭合不变量和基于扩散模型的生成式深度学习框架，实现了对黑洞源结构的稳健约束。该方法在合成数据及 3C279 和半人马射电源A（Centaurus A）的实际观测数据中均表现出良好的成像潜力。

arXiv:2608.19675v1 Announce Type: new Abstract: The Event Horizon Telescope (EHT) Collaboration's images of the supermassive black holes in M87 and the Milky Way have provided the first event-horizon-scale views of these objects, opening new avenues for studies of gravitation, accretion physics, and black hole astrophysics. Achieving these results, however, requires imaging under some of the most challenging conditions in radio astronomy, including low signal-to-noise ratios, severe calibration uncertainties, and sparse aperture coverage. With the aim of presenting independent analyses of the public EHT datasets for M87* and Sgr A*, we adopt an approach that is independent in observables, and reconstruction methodology. Our framework is based on closure invariants, a class of interferometric observables that are intrinsically immune to station-based calibration errors and therefore provide robust constraints on source structure. We combine these observables with Generative Deep learning Image Reconstruction with Closure Terms (GenDIReCT), a diffusion-based image reconstruction framework that operates in the latent space of images conditioned on closure invariants. We present independent reconstructions obtained using GenDIReCT on synthetic challenge data sets as well as real EHT data on 3C279 and Centaurus A, and compare them with previously reported results. This work demonstrates the potential of closure-invariant-driven generative imaging as