基于超图表示学习和图条件扩散的碰撞事件综合重建
Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion
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中文总结 AI 辅助
VyPER利用超图表示和扩散模型统一解决碰撞事件中的粒子分配与中微子运动学预测，在多个质子-质子过程中实现精确重建，助力标准模型精密测量。
中文摘要 AI 辅助
在粒子对撞机实验中，事件重建是从探测器记录的稳定末态推断硬散射中产生的短寿命粒子运动学的任务。我们将事件重建分解为两个主要任务：将测量的喷注和带电轻子分配给母粒子，以及预测未测量的中微子运动学。我们提出了VyPER，一种新颖的几何学习框架，它将碰撞事件表示为具有物理启发拓扑的超图。VyPER将用于粒子分配的超边监督分类与用于预测中微子运动学的扩散模型相结合，利用联合损失函数在统一框架内优化两个重建任务。我们在多个质子-质子碰撞过程中展示了VyPER，并将其性能与现有的基于分析和机器学习的重建技术进行了比较。通过这样做，我们证明了在广泛的标准模型物理过程中可以实现准确的事件重建，为希格斯玻色子、电弱和顶夸克领域的精密测量开辟了新途径。
英文摘要
In particle collider experiments, event reconstruction is the task of inferring the kinematics of short-lived particles produced in the hard scatter from the stable final states recorded by detectors. We decompose event reconstruction into two primary tasks: assigning measured jets and charged leptons to parent particles, and predicting unmeasured neutrino kinematics. We present VyPER, a novel geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology. VyPER combines the supervised classification of hyperedges for particle assignment with a diffusion model for predicting neutrino kinematics, leveraging a joint loss function to optimize both reconstruction tasks within a unified framework. We showcase VyPER across several proton-proton collision processes, comparing its performance to existing analytical and machine-learning-based reconstruction techniques. In doing so, we demonstrate that accurate event reconstruction is achievable across a diverse range of Standard Model physics processes, opening new avenues for precision measurements in the Higgs boson, electroweak, and top-quark sectors.
发表机构
- Shanghai Jiao Tong University（上海交通大学）
- University of Manchester（曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
