一种基于机器学习动量测量的宇宙缪子断层扫描系统，用于多物体重建与材料表征
A Cosmic Muon Tomography System with Machine Learning based Momentum Measurement for Multi-Object Reconstruction and Material Characterization
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中文总结 AI 辅助
本研究开发了一套结合GEANT4框架、HDBSCAN算法及散射密度参数的宇宙缪子断层扫描系统，可实现多物体的检测、定位与材料鉴别，适用于安全筛查等领域。
中文摘要 AI 辅助
宇宙缪子断层扫描是一种强大的非破坏性成像技术，可通过多次库仑散射对致密且受屏蔽的材料进行检测。本研究中，我们提出了一套完整缪子断层扫描系统的设计、模拟与性能评估方案，该系统包含六个用于轨迹重建的闪烁体条探测器站，以及一个用于缪子动量估计的四站磁谱仪。探测器几何结构在GEANT4框架中实现，并针对物体定位与材料表征进行了优化。重建得到的动量与散射角结合，定义了散射密度ρₛ=(θp)²/L_eff，该参数增强了对材料相关散射的敏感性。最近接近点（PoCA）重建用于估计成像体积内的散射位置。为检测并分离多个未知物体，我们对重建的PoCA点云应用了基于密度的带噪声应用空间聚类（HDBSCAN）算法。随后提取聚类级别的散射与几何特征以进行物体表征。所提出的框架可在统一分析流程内实现物体检测、定位、体积估计、形状重建及材料排序。对多种不同成分物体的模拟研究表明，该系统可准确重建物体位置与几何结构，同时基于散射密度提供可靠的材料鉴别能力。该开发的系统为下一代宇宙缪子断层扫描应用提供了可扩展的方案，可应用于安全筛查、核废料表征及非破坏性检测领域。
英文摘要
Cosmic muon tomography is a powerful non-destructive imaging technique for inspecting dense and shielded materials through multiple Coulomb scattering. In this work, we present the design, simulation, and performance evaluation of a complete muon tomography system comprising six scintillator-strip tracking stations for trajectory reconstruction and a four-station magnetic spectrometer for muon momentum estimation. The detector geometry is implemented in the GEANT4 framework and optimized for object localization and material characterization. The reconstructed momentum is combined with the scattering angle to define the scattering density $ρ_s = {(θp)^2}/{L_{\mathrm{eff}}}$, which enhances sensitivity to material-dependent scattering. Point-of-Closest-Approach (PoCA) reconstruction is used to estimate scattering locations within the imaging volume. To detect and separate multiple unknown objects, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to the reconstructed PoCA cloud. Cluster-level scattering and geometric features are then extracted for object characterization. The proposed framework enables object detection, localization, volume estimation, shape reconstruction, and material ranking within a unified analysis pipeline. Simulation studies with multiple objects of different compositions demonstrate accurate reconstruction of object positions and geometries, while providing reliable material discrimination based on scattering density. The developed system offers a scalable approach for next-generation cosmic muon tomography applications in security screening, nuclear waste characterization, and non-destructive inspection.
