Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
This paper proposes a self-supervised sparse ViT framework that leverages masked autoencoding and relational objectives to learn reusable representations from heterogeneous neutrino detector data, significantly improving reconstruction performance and data efficiency for energy-frontier experiments like FASERCal while demonstrating strong transferability across diverse detector technologies.