用生成模型从真实对撞数据中自动学出标准模型物理结构。
Learning Standard Model structure from LHC data with Riemannian flow matching
- 基于黎曼流匹配的Transformer模型，按壳上流形生成粒子。
- 仅用10亿事件训练，就复现了多个共振峰和质量参数。
- 无需先验知识，适合高能物理数据挖掘与模型验证。
本文展示了一个基于Transformer的生成模型，能够捕捉跨越五个数量级质心能量（从亚GeV到TeV连续区）的标准模型结构，这一范围远超单一蒙特卡洛样本覆盖能力。为此，我们设计了 extsc{ShellFlow}——一种基于黎曼条件流匹配的模型，给定事件组成后，将每个粒子生成在其壳上流形上。其唯一物理先验是壳上条件与不变质量公式。模型在约10^9个来自ATLAS开放数据13 TeV释放的真实质子-质子碰撞事件上训练，且未提供其他信息。单次训练即成功重现：粒子内部运动学、双轻子共振峰（J/ψ、Υ、Z）在PDG标定位置、轻子型韦内伯角、W玻色子与顶夸克质量，以及未显式包含于训练目标中的粒子间关联。因此，标准模型的大量内容可直接从记录的对撞数据中学习获得。
原文摘要 · Abstract (English)
In this work we demonstrate that a single transformer-based generative model can capture Standard Model structure spanning five decades of invariant mass, from the sub-GeV regime to the TeV continuum, a range that no single Monte Carlo sample covers. To achieve this we design \textsc{ShellFlow}, a Riemannian conditional flow matching model that, given the recorded event composition, generates each particle on its on-shell manifold. Its only physics priors are the on-shell condition and the invariant-mass formula. The model is trained on $\sim 10^{9}$ real $pp$ collision events from the ATLAS Open Data 13~TeV release and told nothing else. From a single training run, the model learns to reproduce all of the following: intra-particle kinematics, the dilepton resonances ($J/ψ$, $Υ$, $Z$) at their PDG positions, the leptonic Weinberg angle, the $W$ and top-quark masses, and inter-particle correlations that enter no training objective. A substantial fraction of the Standard Model is thus learnable directly from recorded collision data.
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