用混合模态流模型生成高能对撞机喷注，兼顾连续动量与离散量子数。
Multimodal Generative Flows for LHC Jets
- 基于Transformer的连续时间马尔可夫跳跃桥，统一建模粒子动量与量子数
- 在CMS开放数据上训练，生成喷注在动量、子结构和味组成上均逼真
- 适合高能物理模拟与异常检测，尤其关注多模态粒子数据建模
大型强子对撞机（LHC）高能碰撞的生成建模为仿真、异常检测等提供了数据驱动路径。核心挑战在于粒子云数据的混合特性：每个粒子携带连续的运动学特征和离散的量子数（如电荷、味）。本文提出一种基于Transformer的多模态流模型，通过连续时间马尔可夫跳跃桥扩展流匹配方法，联合建模包含两种模态的LHC喷注。在CMS开放数据上训练后，该模型可生成具有真实动量分布、喷注子结构和味组成特征的高质量喷注。
原文摘要 · Abstract (English)
Generative modeling of high-energy collisions at the Large Hadron Collider (LHC) offers a data-driven route to simulations, anomaly detection, among other applications. A central challenge lies in the hybrid nature of particle-cloud data: each particle carries continuous kinematic features and discrete quantum numbers such as charge and flavor. We introduce a transformer-based multimodal flow that extends flow-matching with a continuous-time Markov jump bridge to jointly model LHC jets with both modalities. Trained on CMS Open Data, our model can generate high fidelity jets with realistic kinematics, jet substructure and flavor composition.
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