EveNet用5亿事件训练，让粒子对撞数据分析更高效精准。
EveNet: A Foundation Model for Particle Collision Data Analysis
- 基于粒子云共享表征，融合自监督与物理约束预训练。
- 在重共振态和奇异希格斯衰变搜索中超越现有模型，低统计下仍有效。
- 可迁移至实验数据，稳定提取量子关联量，适合高能物理研究者。
深度学习正重塑高能物理数据分析，但计算瓶颈限制其潜力。我们提出EveNet，一个面向对撞机物理的事件级基础模型，通过5亿个模拟碰撞事件预训练，采用自监督学习与物理引导监督相结合的混合目标。该模型利用共享粒子云表示，在多重任务中表现优异，包括重共振态与奇异希格斯衰变搜索，并在低统计条件下展现卓越数据效率。关键的是，我们验证了模型对实验数据的可迁移性，成功在CMS开放数据中复现Υ介子信号，并通过稳健提取对系统误差不敏感的量子关联观测量，展示了其在精密物理中的应用潜力。结果表明，EveNet能有效编码粒子相互作用的基本物理结构，为当前及未来对撞机提供统一、高效的加速发现框架。
原文摘要 · Abstract (English)
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.
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