Conditional Image Diffusion with Interferometric Closure Invariants: Independent EHT Imaging of Centaurus~A and 3C~279

TL;DR

This paper introduces GenDIReCT, a machine learning framework using conditional diffusion models and interferometric closure invariants for independent, calibration-free imaging of EHT observations, revealing detailed structures of Centaurus A and 3C 279.

Contribution

The paper presents a novel generative deep learning approach that leverages closure invariants for independent, robust imaging of VLBI data, reducing calibration systematics and enabling sampling of plausible images.

Findings

Revealed emission ridges in Centaurus A's jet sheath.

Identified superluminal motion in 3C 279's jet ejecta.

Demonstrated closure invariants preserve morphological information.

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 (GenDIReCT), 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 ($\simeq 80\,\mu$as each) along the jet sheath with a brightness ratio of $1.4\pm 0.1$ and an opening angle of $12.3\pm 0.3$~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 $4.6\pm 1.0\,\mu$as over $\approx 5.39$ days away from the northern components, corresponding to an apparent superluminal velocity of $\simeq 10\pm 2$ 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 GenDIReCT further allows us to sample and characterise clusters of plausible image solutions for each dataset. As a calibration-independent, generative imaging approach, GenDIReCT offers a robust and truly independent blind-imaging tool for current and future VLBI experiments.