Bayesian inference of high-purity germanium detector impurities based on 
capacitance measurements and machine-learning accelerated capacitance 
calculations

Abt, I.; Gooch, C.; Hagemann, F.; Hauertmann, L.; Liu, X.; Schuster, O.Schulz.M.

doi:10.1140/epjc/s10052-023-11509-8

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Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations

Abt, I., Gooch, C., Hagemann, F., Hauertmann, L., Liu, X., & Schuster, O. (2023). Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations. European Physical Journal C, 83, 352. doi:10.1140/epjc/s10052-023-11509-8.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000F-1206-6 Version Permalink: https://hdl.handle.net/21.11116/0000-000F-1207-5

Genre: Journal Article

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Creators:
Abt, I.¹, Author
Gooch, C.¹, Author
Hagemann, F.¹, Author
Hauertmann, L.¹, Author
Liu, X.¹, Author
Schuster, O.Schulz.M.¹, Author

Affiliations:
1Max Planck Institute for Physics, Max Planck Society and Cooperation Partners, ou_2253650

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Abstract: 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.}, issn={1434-6052},

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Dates: Date issued: 2023

Publication Status: Issued

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Identifiers: Other: MPP-2023-414
URI: https://publications.mppmu.mpg.de/?action=search&mpi=MPP-2023-414
DOI: 10.1140/epjc/s10052-023-11509-8

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Title: European Physical Journal C

Abbreviation : Eur.Phys.J.C

Source Genre: Journal

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Pages: - Volume / Issue: 83 Sequence Number: - Start / End Page: 352 Identifier: -