arXiv 2602.080102026-02-08hep-ex

在CMS实验中利用机器学习进行在线和离线重建与识别

Machine learning in online and offline reconstruction and identification with CMS

Department, University, Street number, City, Country

Uttiya Sarkar

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

CMS实验利用机器学习技术提升在线和离线事件重建与识别的性能，包括喷注风味标记、tau粒子识别和能量重建，并展望未来在高粒度量能器中进一步应用ML技术以应对极端堆叠条件。

摘要：

机器学习（ML）在CMS实验的在线和离线事件重建与识别中扮演着越来越重要的角色。各种ML技术被用来提高物理对象的识别能力。专门的算法增强了喷注风味标记，包括新的方法，这些方法增强了对底夸克衰变到希格斯玻色子的敏感性。通过ML方法，tau粒子的识别得到了显著改善，而在电磁量能器中，由ML驱动的聚类技术提供了更好的能量重建。缪子识别也从多变量方法中受益，导致信号效率更高且背景排斥更有效。展望未来，ML将在高粒度量能器的重建策略中发挥核心作用。正在开发新的算法用于升级的探测器，以应对极端的堆叠条件。所有这些进展确保CMS能够充分利用Run-3和HL-LHC的物理潜力，同时探索新的ML策略以在不断变化的实验条件下保持稳健的性能。

英文摘要：

Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated algorithms enhance jet flavor tagging, including new approaches that strengthen sensitivity to Higgs boson decays to charm quarks. Tau identification has been significantly improved with ML-based methods, while in the electromagnetic calorimeter, ML-driven clustering techniques provide better energy reconstruction. Muon identification also benefits from multivariate approaches, leading to a higher signal efficiency and more background rejection. Looking at the future, ML will be central to the reconstruction strategy for the High-Granularity Calorimeter at high-luminosity LHC. New algorithms for the upgraded detectors are being developed to cope with extreme pileup conditions. All these advances ensure that CMS can fully exploit the physics potential of Run-3 and the HL-LHC, while also exploring novel ML strategies to maintain robust performance under evolving experimental conditions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

AI总结：

摘要：

英文摘要：

相关论文