Simulation-to-Real Learning for VLBI Jet Morphology Classification

We investigate simulation-to-real learning for morphological classification of parsec-scale active galactic nucleus (AGNs) jets observed with Very Long Baseline Interferometry (VLBI) across frequencies from 1 to 90 GHz. To overcome the scarcity of labelled VLBI data, we combine real observations with physically motivated synthetic images generated from the Blandford–K\"onigl jet model via relativistic ray tracing and realistic interferometric reconstruction. We compare supervised learning, self-supervised pretraining, and domain adaptation strategies based on a ResNet-18 backbone, and show that explicit domain adaptation substantially improves transfer to real VLBI observations, achieving an $F_1$-score of 0.86 on manually labelled Astrogeo sources. The trained model is subsequently applied to more than 130\,000 unlabelled AGN images, enabling one of the largest parsec-scale morphology studies to date. The inferred populations reveal statistically significant differences in radio brightness between compact and extended sources, suggesting a connection between parsec-scale jet structure and observed radio emission properties.