Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
Abratenkoet al.(MicroBooNE Collaboration), Se- mantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE, Phys
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
hep-ex 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training
Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.