标题： 宇宙μ子断层扫描仪在货物边境控制检查中的优化 显示英文标题

标题： Optimization of a cosmic muon tomography scanner for cargo border control inspection

摘要： 过去几十年来，利用宇宙射线缪子进行未知物体的层析扫描应用取得了显著进展。 最有前景的发展之一是将这种技术应用于边境安全，以检查卡车和海运集装箱内的货物，从而寻找危险和非法的隐藏材料。 本研究专注于类似于欧盟“SilentBorder”项目框架内开发的缪子断层扫描系统的优化研究，该项目由欧盟地平线2020计划资助。 当前的研究方向是优化探测器模块设计，采用两种互补的方法。 第一种方法利用TomOpt，这是一种基于Python的端到端软件，使用可微编程来优化散射断层扫描探测器配置。 虽然TomOpt本身支持基于梯度的优化，但引入了一个贝叶斯优化模块，以更好地处理具有噪声目标函数的情况，特别是在图像重建驱动的优化任务中。 第二种优化策略依赖于基于GEANT4的详细模拟，尽管计算量更大，但提供了更高的物理保真度。 这些模拟还用于研究在宇宙缪子之外结合次级粒子信息对提高材料鉴别能力的影响。 本文介绍了这些优化研究的当前状态和所获得的结果。 显示英文摘要

摘要： The past several decades have seen significant advancement in applications using cosmic-ray muons for tomography scanning of unknown objects. One of the most promising developments is the application of this technique in border security for the inspection of cargo inside trucks and sea containers in order to search for hazardous and illicit hidden materials. This work focuses on the optimization studies for a muon tomography system similar to that being developed within the framework of the `SilentBorder' project funded by the EU Horizon 2020 scheme. Current studies are directed toward optimizing the detector module design, following two complementary approaches. The first leverages TomOpt, a Python-based end-to-end software that employs differentiable programming to optimize scattering tomography detector configurations. While TomOpt inherently supports gradient-based optimization, a Bayesian Optimization module is introduced to better handle scenarios with noisy objective functions, particularly in image reconstruction-driven optimization tasks. The second optimization strategy relies on detailed GEANT4-based simulations, which, while more computationally intensive, offer higher physical fidelity. These simulations are also employed to study the impact of incorporating secondary particle information alongside cosmic muons for improved material discrimination. This paper presents the current status and results obtained from these optimization studies.