发表机构
CERN（欧洲核子研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了点云生成模型在快速量热仪模拟中的进展，提出CaloClouds3、CaloHadronic和AllShowers等模型，分别实现光子、强子及十二种粒子的高效模拟，并引入跨几何迁移学习以大幅减少训练数据需求。
AI 中文摘要
对量热仪簇射进行详细的Geant4模拟是碰撞实验最大的单项计算成本，而高亮度大型强子对撞机（High-Luminosity LHC）将需要比目前产生的模拟事件多约十倍的事件。我们总结了高粒度量热仪生成式点云快速模拟的最新进展。CaloClouds3生成光子（电磁）簇射，与几何无关，在单个CPU上平均运行速度比Geant4快约120倍。CaloHadronic使用Transformer注意力机制将点云扩散方法扩展到跨越电磁和强子量热仪的π介子（强子）簇射。AllShowers在单一模型中统一了十二种粒子类型，其参数远少于专门的基线模型，同时匹配或超过其保真度。最后，我们讨论了跨几何迁移学习，该学习在保持生成性能的同时，所需的训练簇射数量减少了二到三个数量级。
英文摘要
Detailed Geant4 simulation of calorimeter showers is the largest single computing cost of collider experiments, and the High-Luminosity LHC will need about ten times more simulated events than are currently produced. We summarise recent progress in generative point cloud fast simulation for highly granular calorimeters. CaloClouds3 generates photon (electromagnetic) showers, is geometry-independent, and runs on average about 120x faster than Geant4 on a single CPU. CaloHadronic uses transformer attention to extend the point cloud diffusion approach to pion (hadronic) showers spanning the electromagnetic and hadronic calorimeters. AllShowers unifies twelve particle types in a single model with far fewer parameters than the specialised baselines while matching or exceeding their fidelity. We close with cross-geometry transfer learning, which needs two to three orders of magnitude fewer training showers while preserving the generative performance.
CommentsProceedings of the 14th Large Hadron Collider Physics Conference (LHCP 2026), 18-22 May 2026, Paris, France. 6 pages, 4 figures
