This work reports numerical simulations and statistical tests carried out to study the performance of a prototype imaging setup to be developed for material discrimination using Muon Scattering Tomography (MST). The reconstructed images have been processed with a Pattern Recognition Method (PRM) to discriminate the low-Z and high-Z materials. The same method has been applied to examine the performance of the setup in recognizing the shape of the test objects. Design parameters of the setup, such as area of the muon detectors, their physical placement, along with their spatial resolution, etc. have been discussed. To evaluate the efficacy of the newly proposed PRM, the results have been compared to another technique based on cluster finding.
Pattern recognition of $^{136}$Xe double beta decay events and background discrimination in a high pressure Xenon TPC
高压气体探测器为检测罕见事件提供了优势,其中背景减少至关重要。高压氙气时间投影室(TPC)对136氚的无中微子双β衰变具有很好的能量分辨率和详细的拓扑信息。气态氙中136氘的双β衰变事件的电离拓扑结构具有由两个离散电子径迹定义的特征形状,这两个电子径迹最终形成两个较高电离电荷密度的斑点。通过适当的像素化读出,这种拓扑信息对于执行强大的背景区分是非常宝贵的。在这项研究中,我们进行了详细的模拟信号的拓扑结构,以及竞争的拓扑结构,从伽马事件,通常构成在这些能量的背景。我们基于图论概念定义了可观测量,并开发了自动判别算法,该算法将背景水平降低了约三个数量级,同时保持了40%的信号效率。这一结果支持了目前或未来基于气体TPC的双β实验的竞争力,例如目前正在Canfranc Subaragio de Canfranc(LSC)建造的中微子氙TPC(NEXT)。
Full Characterization of a Mock Nuclear Waste Barrel with Muon Tomography using Micromegas Detectors
CEA/Irfu, Université Paris-Saclay(法国原子能委员会/核研究与物理技术研究所,巴黎萨克雷大学);Centro de Astropartículas y Física de Altas Energías (CAPA), Universidad de Zaragoza(高能天体粒子中心,萨拉戈萨大学)
Optimization of a cosmic muon tomography scanner for cargo border control inspection
Université catholique de Louvain(天主教鲁汶大学);University of Sheffield(谢菲尔德大学);Universal Scientific Education and Research Network(通用科学研究与教育网络);University of Tartu(塔尔图大学)