发表机构
Massachusetts Institute of Technology; Universiteit Maastricht; Nikhef National Institute for Subatomic Physics; INFN Laboratori Nazionali di Frascati; INFN Sezione di Bologna（麻省理工学院; 马斯特里赫特大学; 荷兰国家亚原子物理研究所; 意大利国家核物理学院弗拉斯卡蒂国家实验室; 意大利国家核物理学院博洛尼部分院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LHCb Run 3 GPU触发器的实时需求，提出基于Lipschitz约束神经网络的缪子和电子鉴别算法，在广泛运动学区域内提升鉴别性能并满足实时执行约束。
AI 中文摘要
LHCb在Run 3中的物理项目关键依赖于对包含缪子和电子的事件进行高效的实时选择，这些轻子是多种重味和奇异衰变过程中的关键特征。LHCb Run 3探测器现在采用全软件触发系统，以LHC束团交叉速率处理完整的探测器读出，第一级触发在GPU上执行。在这种环境下，粒子鉴别算法必须在满足吞吐量和内存占用严格限制的同时，实现高效率和背景抑制。我们提出了基于Lipschitz约束神经网络的LHCb Run 3 GPU触发器中缪子和电子鉴别算法。分别为缪子和电子开发了独立的网络，并使用模拟事件进行训练。其性能相对于之前的基线算法进行了评估，结果表明在广泛的运动学区域内鉴别能力得到提升，同时满足实时GPU执行的要求。
英文摘要
The LHCb physics program in Run 3 relies critically on the efficient real-time selection of events containing muons and electrons, which are key signatures in a wide range of heavy-flavor and exotic decay processes. The LHCb Run 3 detector now operates with a fully software-based trigger that processes the complete detector readout at the LHC bunch-crossing rate, with the first trigger stage executed on GPUs. In this environment, particle-identification algorithms must achieve high efficiency and background rejection while satisfying stringent constraints on throughput and memory footprint. We present algorithms for muon and electron identification in the LHCb Run 3 GPU trigger based on Lipschitz-constrained neural networks. Separate networks are developed for muons and electrons and are trained using simulated events. Their performance is evaluated relative to the previous baseline algorithms, demonstrating improved discrimination across a wide range of kinematic regions while remaining compatible with the requirements of real-time GPU execution.
Comments11 pages, 4 figures. Prepared for submission to JINST
