Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Accelerator-based neutrino physics is entering an energy-frontier regime in which interactions reach the TeV scale and produce exceptionally dense, overlapping detector signatures. In this regime, event interpretation becomes impractical for conventional reconstruction approaches, particularly when labelled data are scarce and the analysis spans diverse downstream objectives. We present a sparse ViT framework for learning reusable representations from heterogeneous detector data. Self-supervised pre-training combines masked autoencoder reconstruction with relational voxel-level objectives for hierarchy, ghost and particle identification, and the resulting shared encoder is then jointly fi...
| Authors | Saúl Alonso-Monsalve, Fabio Cufino, Umut Kose et al. |
|---|---|
| Primary category | hep-ex |
| Published | 8 April 2026 |
| Last updated at source | 12 August 2026 |
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