Title: Machine Learning for Event Reconstruction in the CMS Phase-2 High Granularity Calorimeter Endcap Show Chinese title

Title: 用于CMS Phase-2高粒度量能器端盖事件重建的机器学习

Abstract: The high-luminosity era of the LHC will offer greatly increased number of events for more precise Standard Model measurements and Beyond Standard Model searches, but will also pose unprecedented challenges to the detectors. To meet these challenges, the CMS detector will undergo several upgrades, including the replacement of the current endcap calorimeters with a novel High-Granularity Calorimeter (HGCAL). To make optimal use of this innovative detector, new and original algorithms are being devised. A dedicated reconstruction framework, The Iterative Clustering (TICL), is being developed within the CMS Software (CMSSW). This new framework is designed to fully exploit the high spatial resolution and precise timing information provided by HGCAL. Several key ingredients of the object reconstruction chain already rely on Machine Learning (ML) techniques and their usage is expected to further develop in the future. The existing reconstruction strategies will be presented stressing the role played by ML techniques to exploit the information provided by the detector. The areas where ML techniques are expected to play a role in the future developments will be also discussed. Show Chinese abstract

Abstract: 高亮度时代的大强子对撞机将提供大量事件，以进行更精确的标准模型测量和超出标准模型的搜索，但也将给探测器带来前所未有的挑战。 为了应对这些挑战，CMS探测器将进行多项升级，包括用一种新型高颗粒度 calorimeter（HGCAL）替换当前的端盖 calorimeters。 为了充分利用这种创新探测器，正在设计新的和原创的算法。 一个专门的重建框架，迭代聚类（TICL），正在CMS软件（CMSSW）中开发。 这个新框架旨在充分利用HGCAL提供的高空间分辨率和精确时间信息。 物体重建链中的几个关键要素已经依赖于机器学习（ML）技术，预计未来其应用将进一步发展。 将介绍现有的重建策略，并强调ML技术在利用探测器提供的信息中所起的作用。 还将讨论未来发展中ML技术预期发挥作用的领域。