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كما كتبه المؤلفون، بالإنجليزية arXiv:2608.19675v1

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 a calibration-resilient framework for Very Long Baseline Interferometry (VLBI) and provides an independent and complementary avenue for interpreting horizon-scale black hole observations.