https://doi.org/10.1140/epjc/s10052-023-11509-8
Regular Article - Experimental Physics
Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations
Max Planck Institut für Physik, Föhringer Ring 6, 80805, Munich, Germany
Received:
27
September
2022
Accepted:
14
April
2023
Published online:
3
May
2023
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.
© The Author(s) 2023
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