发表机构
Stanford University; SLAC National Accelerator Laboratory（斯坦福大学; SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
为 CLD 探测器构建 Parnassus 生成式替代模型，以条件流匹配神经网络模拟探测器模拟与重建，在 $e^+e^-$ 事件上实现高保真度，生成速度比完整模拟快数个数量级，并保留味区分能力。
AI 中文摘要
对于未来的 $e^+e^-$ 对撞机项目，探测器模拟和事件重建将耗费大量计算资源，目前是进行精确可行性研究的关键瓶颈。为解决这一挑战，我们为 CLD 探测器概念构建了一个 Parnassus 模型。Parnassus 是一个自动调整替代模型（在我们的案例中为条件流匹配神经网络）以模拟完整探测器模拟和重建的框架。我们在 $\u221as=91.2$ GeV 下，使用经 Geant4 模拟的 CLD 探测器概念和 Pandora 粒子流重建处理的 $e^+e^-\ o Z\ o q\ar q$ 事件进行训练，并重现了单粒子运动学、粒子标识和撞击参数分布。我们还检查了喷流和事件级特征（包括整体和按味分裂），发现其保真度极佳，显著优于参数化程序 Delphes。此外，我们在重建的粒子流成分上训练了一个基于 transformer 的味标记器，并表明该替代模型保留了完整 CLD 重建的 $b/c/s/q$ 区分能力。Parnassus CLD 模型在单个 GPU（CPU）上每个事件的生成成本约为 1.2 毫秒（40 毫秒），比完整模拟和重建快三个（两个）数量级。我们的模型可公开用于可行性和设计研究。
英文摘要
Detector simulation and event reconstruction will be computationally expensive for future $e^+e^-$ collider programs and are currently critical bottlenecks for accurate feasibility studies. To address this challenge, we build a Parnassus model for the CLD detector concept. Parnassus is a framework for automatically tuning a surrogate model, in our case, a conditional flow matching neural network, to emulate a full detector simulation and reconstruction. We train on $e^+e^-\to Z\to q\bar q$ events at $\sqrt{s}=91.2$ GeV processed through a Geant4 simulation of the CLD detector concept and the Pandora particle-flow reconstruction and reproduce single-particle kinematics, particle-IDs, and impact-parameter distributions. We also examine jet- and event-level features, inclusively and split by flavor, and find excellent fidelity, significantly better than the parameterized program Delphes. Furthermore, we train a transformer-based flavor tagger on the reconstructed particle-flow constituents and show that the surrogate preserves the $b/c/s/q$ discrimination of the full CLD reconstruction. The Parnassus CLD model achieves a generation cost of about 1.2 ms (40 ms) per event on a single GPU (CPU), over three (two) orders of magnitude faster than full simulation and reconstruction. Our model is publicly available for feasibility and design studies.
Comments32 pages, 16 figures, and 9 tables. Data are available at https://huggingface.co/datasets/umarsqur/cld-fcc-ee-fullsim-rec and source code at https://github.com/parnassus-hep/parnassus
