Self-supervised pre-training on multimodal neutrino detector simulations produces reusable representations that improve downstream classification, regression, and data efficiency over training from scratch.
Fine-tuning further uses layer-wise learning-rate decay [55], exponential moving averages of the model parameters [56] and uncertainty- based multi-task weighting [52]
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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.