探索粒状量能器中高能质子-π介子区分的极限

中文摘要 AI 辅助

高粒状量能器提供强子簇射发展的详细信息，可实现超出能量测量常规作用的粒子鉴别。本研究探究该信息区分质子与正π介子的能力，以及可实现的区分效果如何依赖于探测器分段和粒子能量。研究采用Geant4模拟，模拟能量为10至100 GeV的孤立粒子在均质钨酸铅量能器中的情况。直接基于单元位置、探测能量和时间的Deep Sets模型，优于基于重建簇射观测量的增强决策树。在单元尺寸为3×3×6 mm³时，Deep Sets在10 GeV下准确率达93.8%，100 GeV下降至67.2%。簇射拓扑结构具有独立信息，沉积能量提供最大额外贡献，时间提供互补信息。更粗糙的分段会降低区分性能，且性能对纵向粒度的敏感性高于横向粒度。这些结果为基于量能器的强子鉴别提供了令人鼓舞的基准，并推动其被纳入未来高粒状量能器的优化目标中。

英文摘要

Highly granular calorimeters provide detailed information about hadronic-shower development that may enable particle identification beyond their conventional role in energy measurement. We investigate how well this information can distinguish protons from positively charged pions and how the achievable discrimination depends on detector segmentation and particle energy. The study uses Geant4 simulations of isolated particles with energies from 10 to 100 GeV in a homogeneous lead-tungstate calorimeter. A Deep Sets model operating directly on cell positions and detected energy and time outperforms a boosted decision tree based on reconstructed shower observables. With cells measuring $3 \times 3 \times 6$ mm$^3$, Deep Sets achieves an accuracy of 93.8% at 10 GeV, decreasing to 67.2% at 100 GeV. Shower topology is independently informative, deposited energy provides the largest additional contribution, and timing supplies complementary information. Coarser segmentation reduces discrimination, with performance more sensitive to longitudinal than transverse granularity. These results provide an encouraging benchmark for calorimeter-based hadron identification and motivate its inclusion among the optimization targets for future highly granular calorimeters.

发表机构

• University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
• Universidad de Oviedo（奥维耶多大学）
• CERN, European Organization for Nuclear Research（欧洲核子研究组织（CERN））
• University of Padova, Department of Physics and Astronomy（帕多瓦大学物理与天文学系）
• Luleå University of Technology（吕勒奥理工学院）
• Istituto Nazionale di Fisica Nucleare, Sezione di Padova（意大利国家核物理研究所帕多瓦分部）

机构由 AI 辅助整理，请以论文原文为准。