标题： 基于数据的多通道伽马光谱仪性能优化 显示英文标题

标题： Data-driven performance optimization of gamma spectrometers with many channels

摘要： 在具有多个通道（例如许多像素或体素）的伽马能谱仪中，存在一个权衡问题，即包括性能逐渐变差的读出通道与提高效率之间的权衡。 随着通道数量的增长，计算最优包含通道集的暴力方法在计算上是指数级不可行的，因此需要近似方法。 在本工作中，我们提出了一种数据驱动的框架，旨在尝试找到接近最优的包含探测器通道集。 该框架利用非负矩阵分解（NMF）来学习探测器上伽马能谱的行为，并将性能相似的探测器通道聚类在一起。 然后通过移除通道聚类后的能谱进行性能比较，这比暴力方法更可行。 该框架具有通用性，可以根据应用的不同，应用于任意用户定义的性能指标。 我们将该框架应用于优化由H3D M400 CdZnTe能谱仪测量的伽马能谱，这些能谱在其晶体体积内表现出可变的性能。 特别是，我们展示了几个示例，用于优化核保障无损检测中铀和钚伽马能谱的各种性能指标，并探索了性能与聚类算法类型等参数之间的趋势。 我们还将NMF+聚类流程与几种非机器学习算法进行了比较，包括几种贪心算法。 总体而言，我们发现NMF+聚类流程往往能找到性能最佳的探测器体素集，显著优于未优化的能谱，但在某些情况下，通过探测器深度分割的贪心能谱累积可以在大大减少计算时间的情况下获得类似的性能提升。 显示英文摘要

摘要： In gamma spectrometers with variable spectroscopic performance across many channels (e.g., many pixels or voxels), a tradeoff exists between including data from successively worse-performing readout channels and increasing efficiency. Brute-force calculation of the optimal set of included channels is exponentially infeasible as the number of channels grows, and approximate methods are required. In this work, we present a data-driven framework for attempting to find near-optimal sets of included detector channels. The framework leverages non-negative matrix factorization (NMF) to learn the behavior of gamma spectra across the detector, and clusters similarly-performing detector channels together. Performance comparisons are then made between spectra with channel clusters removed, which is more feasible than brute force. The framework is general and can be applied to arbitrary, user-defined performance metrics depending on the application. We apply this framework to optimizing gamma spectra measured by H3D M400 CdZnTe spectrometers, which exhibit variable performance across their crystal volumes. In particular, we show several examples optimizing various performance metrics for uranium and plutonium gamma spectra in nondestructive assay for nuclear safeguards, and explore trends in performance vs.\ parameters such as clustering algorithm type. We also compare the NMF+clustering pipeline to several non-machine-learning algorithms, including several greedy algorithms. Overall, we find that the NMF+clustering pipeline tends to find the best-performing set of detector voxels, significantly improving over the un-optimized spectra, but that a greedy accumulation of spectra segmented by detector depth can in some cases give similar performance improvements in much less computation time.