利用图神经网络重构CMS高密度度量计中的带电π子喷流
仪器与探测器
2024-12-20 v3 高能物理 - 实验
数据分析、统计与概率
摘要
presented一种用于重构CMS高密度度量计（HGCAL）中喷发性子能量的新方法。HGCAL是一种纵向和横向分辨率极细的采样量计。其活性介质为硅传感器和石英瓷片，通过SiPM读取，吸收剂为电磁段中的铅和Cu/CuW，喷发性子段中的钢。喷流重构方法基于图神经网络，运用动态降维网络架构。结果表明，该算法能够捕捉并抵消通常阻碍使用经典重构方法重建喷发性子的主要效应，通过补偿喷流组成能量、空间分布和数目波动来实现。该算法的性能使用2018年CMS HGCAL原型机的测试束数据进行评估，同时配有CALICE AHCAL原型机的一段。该方法消除能量泄漏影响的能力也得到了证明。
引用
@article{arxiv.2406.11937,
title = {Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter},
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journal= {arXiv preprint arXiv:2406.11937},
year = {2024}
}