Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
The research developed a sparse Vision Transformer framework to learn reusable representations from complex, heterogeneous neutrino detector data. By combining masked autoencoder reconstruction with relational voxel-level objectives, the model learns meaningful features for energy-frontier events, improving performance and data efficiency across multiple downstream physics tasks.