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
本研究利用Parnassus框架，将在ALEPH探测器上预训练的模型迁移至SLD探测器，通过微调显著提升粒子与喷注级重建精度，验证了跨探测器迁移学习的可行性，并发布了AI就绪的SLD模拟数据。
AI 中文摘要
Parnassus是一个快速的探测器模拟与重建框架，能够将真值级粒子直接映射为重建粒子。在本工作中，我们研究了跨探测器迁移学习，即将为ALEPH探测器训练的Parnassus模型适配到SLD探测器。ALEPH和SLD具有相似的探测器响应，因为它们都针对强子Z极点正负电子对撞事件，但在底层探测器技术、重建方法和存档数据表示上存在显著差异。我们使用在ALEPH上学习到的权重初始化SLD粒子模型，在SLD数据上进行微调，并将其与直接在SLD上训练的相同架构进行比较。经过ALEPH初始化的模型在粒子级和喷注级上与SLD参考的一致性显著提高，其中带电粒子角响应的改进因子为5.2，喷注角分辨率从SLD参考的1.73倍改进到1.15倍。结果表明，在ALEPH上学到的探测器响应信息可以复用于SLD建模，这为遗留的正负电子对撞探测器（尤其是那些无法访问原始软件管线的实验）提供了可复用的预训练Parnassus模型。本文还发布了AI就绪版的模拟SLD正负电子对撞事件数据。
英文摘要
Parnassus is a fast detector-simulation and reconstruction framework that maps truth-level particles directly to reconstructed particles. In this work, we investigate cross-detector transfer learning by adapting a Parnassus model for the ALEPH detector to the SLD detector. ALEPH and SLD have similar detector responses, as they were both targeting hadronic $Z$-pole $e^+e^-$ events, while differing substantially in the underlying detector technology, reconstruction, and archived data representation. We initialize the SLD particle model with weights learned on ALEPH, fine-tune it on SLD, and compare it with the same architecture trained directly on SLD. The ALEPH-initialized model gives substantially improved particle- and jet-level agreement with the SLD reference, including a factor of 5.2 improvement in the charged-particle angular response and an improvement in the jet angular resolution from $1.73$ to $1.15$ times the SLD reference. The results show that detector-response information learned on ALEPH can be reused when modeling SLD and motivate reusable pretrained Parnassus models for legacy $e^+e^-$ detectors, which is especially critical for experiments without access to the original software pipeline. With this paper, we also release an AI-ready version of simulated SLD $e^+e^-$ events.
Comments14 pages, 12 figures
