AI and the World's Hard Problems · Physics · Nature Machine Intelligence · 2026-09-30
Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pretraining
Event interpretation in energy-frontier neutrino detectors is impractical for conventional reconstruction and hard for supervised models when labelled data are scarce. The authors pretrain a sparse Vision Transformer with masked-autoencoder reconstruction and relational voxel-level objectives, then fine-tune it jointly on classification and regression. On simulated events from the proposed FASER calorimeter, pretraining improved flavour and charm-quark identification, momentum regression and vertex reconstruction, and with about 1,000 labelled events the pretrained encoder matched the flavour-classification performance of a model trained from scratch.