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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17126 |
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| _version_ | 1866914276906631168 |
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| author | Hsu, Ting-Hsiang Zhou, Bai-Hong Liu, Qibin Xu, Yue Li, Shu Hou, George Wei-Shu Nachman, Benjamin Hsu, Shih-Chieh Mikuni, Vinicius Chou, Yuan-Tang Zhang, Yulei |
| author_facet | Hsu, Ting-Hsiang Zhou, Bai-Hong Liu, Qibin Xu, Yue Li, Shu Hou, George Wei-Shu Nachman, Benjamin Hsu, Shih-Chieh Mikuni, Vinicius Chou, Yuan-Tang Zhang, Yulei |
| contents | While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a shared particle-cloud representation, EveNet outperforms state-of-the-art baselines across diverse tasks, including searches for heavy resonances and exotic Higgs decays, and demonstrates exceptional data efficiency in low-statistics regimes. Crucially, we validate the transferability of the model to experimental data by rediscovering the $Υ$ meson in CMS Open Data and show its capacity for precision physics through the robust extraction of quantum correlation observables stable against systematic uncertainties. These results indicate that EveNet can successfully encode the fundamental physical structure of particle interactions, which offers a unified and resource-efficient framework to accelerate discovery at current and future colliders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17126 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EveNet: A Foundation Model for Particle Collision Data Analysis Hsu, Ting-Hsiang Zhou, Bai-Hong Liu, Qibin Xu, Yue Li, Shu Hou, George Wei-Shu Nachman, Benjamin Hsu, Shih-Chieh Mikuni, Vinicius Chou, Yuan-Tang Zhang, Yulei High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a shared particle-cloud representation, EveNet outperforms state-of-the-art baselines across diverse tasks, including searches for heavy resonances and exotic Higgs decays, and demonstrates exceptional data efficiency in low-statistics regimes. Crucially, we validate the transferability of the model to experimental data by rediscovering the $Υ$ meson in CMS Open Data and show its capacity for precision physics through the robust extraction of quantum correlation observables stable against systematic uncertainties. These results indicate that EveNet can successfully encode the fundamental physical structure of particle interactions, which offers a unified and resource-efficient framework to accelerate discovery at current and future colliders. |
| title | EveNet: A Foundation Model for Particle Collision Data Analysis |
| topic | High Energy Physics - Experiment Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2601.17126 |