Foundation model for particle collision data analysis

Ting-Hsiang Hsu; Bai-Hong Zhou; Qibin Liu; Yue Xu; Shu Li; George Wei-Shu Hou; Benjamin Nachman; Shih-Chieh Hsu

hep-exJan 23, 2026

Foundation model for particle collision data analysis

Authors: Ting-Hsiang Hsu, Bai-Hong Zhou, Qibin Liu, Yue Xu, Shu Li, George Wei-Shu Hou, Benjamin Nachman, Shih-Chieh Hsu,

Organizations: Department of Physics, National Taiwan University, Taipei, Taiwan · Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, China · Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, USA · Department of Physics, University of Washington, Seattle, Washington, USA ·

Abstract

Data-intensive collider experiments increasingly require models that transfer across high-dimensional, partially observed measurements, yet most analyses still optimize separate models for each task. Here, to address this bottleneck, we introduce EVENET, an event-level foundation model pretrained on 500 million simulated collision events using self-supervised learning and physics-informed supervision. The model constructs a shared particle-cloud representation from the variable-length set of physics objects in each event. Across searches for heavy resonances and exotic Higgs decays, EVENET outperforms both established and state-of-the-art baselines, particularly in low-statistics regimes. We validate transfer to experimental data by rediscovering the $Υ$ meson in CMS Open Data. The pretrained model also supports precise extraction of quantum-correlation observables that remain stable under the tested systematic variations. Together, these results establish a reusable backbone for diverse collider analyses, improving downstream adaptation after a substantial one-time pretraining investment.

High-Energy Physics

High-energy physics studies the fundamental particles and forces of nature through collider, neutrino, cosmic-ray, and theory-driven experiments.

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Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions

Gregor Krzmanc, Vinicius Mikuni, Benjamin Nachman, Callum Wilkinson

Department of Physics, Stanford University, Stanford, CA 94305, USA · Nagoya University, Kobayashi-Maskawa Institute, Aichi 464-8602, Japan · Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA+1

Cross-Domain Transfer Learning High-Energy Physics

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Future AI-based studies in particle physics will likely start from a foundation model to accelerate training and enhance sensitivity. As a step toward a general-purpose foundation model for particle physics, we investigate whether the OmniLearned and ParticleViT foundation models pretrained on diverse high-

Q^2

simulated and real

pp

and

ep

collisions retain useful knowledge to a few-GeV fixed-target neutrino experiment. We process MINERvA neutrino--nucleus scattering events and evaluate pretrained 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}}

). Pretrained OmniLearned and ParticleViT models outperform similarly sized models trained from scratch at the same compute budget, with the largest gains for OmniLearned on regression and for ParticleViT on classification. When the same transformer architecture is instead initialized from unrelated text pretraining (BERT), this advantage appears only marginally for classification in terms of compute efficiency and not in any way for regression. 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 detector-agnostic inference in particle physics.

Abstract

High-Energy Physics

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