Speaker
Description
Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step towards a general-purpose foundation model for particle physics, we investigate whether the OmniLearned foundation model pre-trained on diverse high-$Q^2$ simulated and real $pp$ and $ep$ collisions can be effectively transferred to a few-GeV fixed-target neutrino experiment. We process MINERvA neutrino-nucleus scattering events and evaluate pre-trained models on two types of tasks: regression of available energy and binary classification of charged-current pion final states ($\mathrm{CC1\pi^{\pm}}$, $\mathrm{CCN\pi^{\pm}}$, and $\mathrm{CC1\pi^{0}}$). At the 3M-parameter scale, pre-trained OmniLearned-small consistently outperforms similarly sized scratch-trained models at matched compute budget and training steps. These results suggest that particle-level foundation models acquire inductive biases that generalize across energy scale, detector technology, and underlying physics, pointing toward a paradigm of detector-agnostic inference in particle physics.
| Contribution types | Long talk (30min + 10min Q/A) |
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