Abstract We present independent imaging analyses of Event Horizon Telescope (EHT) observations of the active galactic nuclei in radio galaxy Centaurus A and quasar 3C 279 using Generative Deep learning Image Reconstruction with Closure Terms (G en DIR e CT), a recently developed machine-learning framework built on conditional diffusion models that uses interferometric closure invariants as primary observables. For Centaurus A, our reconstruction reveals two prominent emission ridges (≃ 80 μas each) along the jet sheath with a brightness ratio of 1.4 ± 0.1 and an opening angle of 12.3 ± 0.3 deg. For 3C 279, we identify three distinct components in the image, with the southern jet ejecta on sub-parsec scale exhibiting a proper motion of 4.6 ± 1.0 μas over ≈ 5.39 days away from the northern components, corresponding to an apparent superluminal velocity of ≃ 10 ± 2 times light speed. These measurements are consistent with those reported by the EHT Collaboration. The results are significant because we demonstrate that: (1) imaging from interferometric aperture synthesis data, especially in VLBI and most acutely in extremely sparse arrays like the EHT, remains a severely ill-posed and challenging inverse problem, yet closure invariants preserve robust morphological information that can strongly constrain structural features, and (2) more importantly, closure-invariant imaging largely avoids calibration systematics, thus providing a fundamentally independent view of spatial structure with very high angular resolution. The generative nature of G en DIR e CT further allows us to sample and characterise clusters of plausible image solutions for each dataset. As a calibration-independent, generative imaging approach, G en DIR e CT offers a robust and truly independent blind-imaging tool for current and future VLBI experiments.
Lai et al. (Tue,) studied this question.