Lens-LeJEPA: Learning the Right Invariances for Strong Gravitational Lenses

Strong gravitational lenses probe small-scale dark matter structure, but labelled simulations are expensive and labelled real lenses barely exist. Self-supervised pretraining should help, yet its usual augmentations are physically wrong here: zooming a lens image rescales the Einstein radius. \textbf{Lens-LeJEPA} replaces them with two lensing priors on a teacher-free joint-embedding objective, exact $D_4$ correspondence between image patches and consistency of arc-weighted descriptors pooled in lens-centred rings. One encoder, pretrained on 87.5k unlabelled images and then frozen, serves three tasks through low-rank adapters. Under a matched budget the priors add 11.6 accuracy points at 100 labels per class and beat the previous lensing-specific JEPA by 13 to 30 points, while rank-stabilized LoRA matches full fine-tuning with 11\% of the weights. We also report where the approach loses, on axion-mass regression, and argue both outcomes follow from the same invariance.