论文标题
基于电容测量和机器学习加速电容计算的贝叶斯推断高纯锗检测器杂质
Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations
论文作者
论文摘要
高纯度锗探测器中的杂质密度对于理解和模拟此类探测器至关重要。但是，基于Hall效应测量值提供的有关制造商提供的杂质的信息通常仅限于几个位置，并且具有较大的不确定性。由于检测器的电容矩阵的电压依赖性在很大程度上取决于杂质密度分布，因此电容测量可以为改善杂质的知识提供途径。此处介绍的新方法使用机器学习的替代模型，该模型对精确的GPU加速电容计算进行了训练，从而从电容测量中执行了杂质分布参数的完整贝叶斯推断。所有步骤均使用开源朱莉娅软件包。电容是用SolidStatedEtector.jl计算的，使用BAT.JL进行的Flux.jl和Bayesian推理完成了机器学习。解释了检测器的电容矩阵及其对杂质密度的依赖性，并提出了N型真实固也是真正的测试检测器的电容偏置电压扫描。研究表明，测试检测器的杂质密度也具有径向依赖性。
The impurity density in high-purity germanium detectors is crucial to understand and simulate such detectors. However, the information about the impurities provided by the manufacturer, based on Hall effect measurements, is typically limited to a few locations and comes with a large uncertainty. As the voltage dependence of the capacitance matrix of a detector strongly depends on the impurity density distribution, capacitance measurements can provide a path to improve the knowledge on the impurities. The novel method presented here uses a machine-learned surrogate model, trained on precise GPU-accelerated capacitance calculations, to perform full Bayesian inference of impurity distribution parameters from capacitance measurements. All steps use open-source Julia software packages. Capacitances are calculated with SolidStateDetectors.jl, machine learning is done with Flux.jl and Bayesian inference performed using BAT.jl. The capacitance matrix of a detector and its dependence on the impurity density is explained and a capacitance bias-voltage scan of an n-type true-coaxial test detector is presented. The study indicates that the impurity density of the test detector also has a radial dependence.
