Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
This work demonstrates that particle physics foundation models can generalize across vastly different energy scales and detector technologies, enabling detector-agnostic inference for the particle physics community.
The authors investigate whether the OmniLearned foundation model, pre-trained on high-energy pp and ep collisions, can transfer to low-energy neutrino-nucleus scattering in MINERvA. Pre-trained models outperform scratch-trained models on regression and classification tasks, achieving better performance at the same compute budget.
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 ($\mathrm{CC1π^{\pm}}$, $\mathrm{CCNπ^{\pm}}$, and $\mathrm{CC1π^{0}}$). Pre-trained OmniLearned models consistently outperform similarly sized models trained from scratch, achieving better overall performance at the same compute budget, as well as achieving better performance at the same number of training steps. These results suggest that particle-level foundation models acquire inductive biases that generalize across large differences in energy scale, detector technology, and underlying physics processes, pointing toward a paradigm of detector-agnostic inference in particle physics.