发表机构
University of Pisa and INFN Pisa, Italy; École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Centro Brasileiro de Pesquisas Fisicas (CBPF), Brazil; Universitat de Barcelona, Spain（比萨大学和意大利国家核物理研究所比萨分部; 洛桑联邦理工学院; 巴西物理研究中心; 巴塞罗那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种用于FCC-ee的图神经网络原型，通过将事件表示为图并利用带电粒子信息，同时识别和重建$b$强子衰变树，在测试样本上实现90%的衰变链正确识别率，且对多代衰变链的聚类效率超过93%。
AI 中文摘要
本文介绍了一种基于深度学习的全事件解释原型，该原型针对正负电子未来环形对撞机（FCC-$ee$）开发，使用了在$\sqrt{s} = 91 \rm{GeV}$下模拟的$e^{+}e^{-}\rightarrow Z\rightarrow b\bar{b}$事件。研究以创新电子正电子加速器探测器（IDEA）概念作为未来应用的案例研究。提出的图神经网络框架旨在利用重建带电粒子的信息，同时识别和重建每个事件中$b$强子的分层衰变树。事件被表示为图，其中重建的带电粒子构成节点，成对观测量被编码为描述粒子之间关系的边特征。在测试样本上，90%的重建衰变链包含完全正确的粒子集合，无论其推断的衰变层次如何。在一系列基准$b$强子衰变中，对于跨越至多三代衰变的衰变链，这种聚类效率超过93%。
英文摘要
This paper presents a deep learning-based full event interpretation prototype developed for the electron-positron Future Circular Collider (FCC-$ee$) using simulated $e^{+}e^{-}\rightarrow Z\rightarrow b\bar{b}$ events at $\sqrt{s} = 91 \ \mathrm{GeV}$. The study was carried out using the Innovative Detector for Electron positron Accelerators (IDEA) detector concept as case study for future application. The proposed graph neural network framework aims to simultaneously identify and reconstruct the hierarchical decay trees of $b$-hadrons in each event using information from reconstructed charged particles. Events are represented as graphs, where reconstructed charged particles form the nodes and pairwise observables are encoded as edge features describing the relations between particles. On a test sample, 90% of reconstructed decay chains contain exactly the correct set of particles, irrespective of their inferred decay hierarchy. Across a range of benchmark $b$-hadron decays, this clustering efficiency exceeds 93% for decay chains spanning up to three generations of decays.
Comments15 pages, 14 figures
