论文标题
HL-LHC的成像量热仪中的重建
Reconstruction in an imaging calorimeter for HL-LHC
论文作者
论文摘要
2027年高光度LHC的CMS端盖热量计升级使用硅传感器实现辐射耐受性，并进一步受益于很高的读数粒度。带有单个SIPM读数的小闪烁瓷砖在辐射水平允许的区域中使用。正在开发一个重建框架，以完全利用检测器的粒度和其他重要特征，例如精确时机，尤其是在HL-LHC的高堆积环境中。迭代聚类框架（TICL）已经建立到位，并正在积极开发。该框架将沉积在线索算法传递的单个热量计层中沉积的能量簇作为输入，该算法最近经过修订和调整。考虑到HL-LHC时代的计算能力的预计极端压力，这些算法是考虑到现代平行体系结构的设计。最近通过在GPU上运行聚类算法获得了重要的加速。正在开发机器学习技术并将其集成到重建框架中。本文将描述所考虑的方法，并显示第一个结果。
The CMS endcap calorimeter upgrade for the High Luminosity LHC in 2027 uses silicon sensors to achieve radiation tolerance, with the further benefit of a very high readout granularity. Small scintillator tiles with individual SiPM readout are used in regions permitted by the radiation levels. A reconstruction framework is being developed to fully exploit the granularity and other significant features of the detector like precision timing, especially in the high pileup environment of HL-LHC. An iterative clustering framework (TICL) has been put in place, and is being actively developed. The framework takes as input the clusters of energy deposited in individual calorimeter layers delivered by the CLUE algorithm, which has recently been revised and tuned. Mindful of the projected extreme pressure on computing capacity in the HL-LHC era, the algorithms are being designed with modern parallel architectures in mind. Important speedup has recently been obtained for the clustering algorithm by running it on GPUs. Machine learning techniques are being developed and integrated into the reconstruction framework. This paper will describe the approaches being considered and show first results.
