Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations
Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations
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基于电容测量和机器学习加速电容计算的高纯度锗探测器杂质的贝叶斯推断
DOI：
10.1140/epjc/s10052-023-11509-8
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Schuster, M.
中科院分区：
文献类型：
--
作者：
Abt, I.;Gooch, C.;Hagemann, F.;Hauertmann, L.;Liu, X.;Schulz, O.;Schuster, M.
高纯度锗探测器中的杂质密度是理解和模拟高纯度锗探测器的关键。然而，制造商根据霍尔效应测量提供的有关杂质的信息通常仅限于少数几个位置，并且具有很大的不确定性。由于检测器的电容矩阵的电压依赖性强烈地依赖于杂质密度分布，电容测量可以提供一种途径来提高对杂质的认识。本文提出的新方法使用机器学习代理模型，经过精确的gpu加速电容计算训练，从电容测量中执行杂质分布参数的全贝叶斯推断。所有步骤都使用开源的Julia软件包。电容是用固体状态检测器计算的。jl，机器学习是用flux完成的。使用bat .jl执行贝叶斯推理。解释了探测器的电容矩阵及其与杂质密度的关系，给出了ann型真同轴测试探测器的电容偏压扫描。研究表明，测试探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 withSolidStateDetectors.jl, machine learning is done withFlux.jland Bayesian inference performed usingBAT.jl. The capacitance matrix of a detector and its dependence on the impurity density is explained and a capacitance bias-voltage scan of ann-type true-coaxial test detector is presented. The study indicates that the impurity density of the test detector also has a radial dependence.
通过电容电压测量在高度分段 HPGe 探测器中进行空间电荷重建
DOI：
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发表时间：
2011
期刊：
影响因子：
--
作者：
B. Bruyneel;B. Birkenbach;P. Reiter
通讯作者：
P. Reiter
BAT.jl：基于 Julia 的贝叶斯推理工具
DOI：
10.1007/s42979-021-00626-4
发表时间：
2021
期刊：
SN Computer Science
影响因子：
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作者：
O. Schulz;F. Beaujean;Allen Caldwell;Cornelius Grunwald;V. Hafych;K. Kröninger;Salvatore La Cagnina;Lars Röhrig;L. Shtembari
通讯作者：
L. Shtembari
通过电容-电压测量确定高度分段大体积 HPGe 探测器中的空间电荷分布
DOI：
--
发表时间：
2011
期刊：
影响因子：
--
作者：
B. Birkenbach;B. Bruyneel;G. Pascovici;J. Eberth;H. Hess;D. Lersch;P. Reiter;A. Wiens
通讯作者：
A. Wiens