دورية أكاديمية
Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations
| العنوان: | Bayesian inference of high-purity germanium detector impurities based on capacitance measurements and machine-learning accelerated capacitance calculations |
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| المؤلفون: | I. Abt, C. Gooch, F. Hagemann, L. Hauertmann, X. Liu, O. Schulz, M. Schuster |
| المصدر: | European Physical Journal C: Particles and Fields, Vol 83, Iss 5, Pp 1-16 (2023) |
| بيانات النشر: | SpringerOpen, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Astrophysics LCC:Nuclear and particle physics. Atomic energy. Radioactivity |
| مصطلحات موضوعية: | Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1434-6052 |
| Relation: | https://doaj.org/toc/1434-6052 |
| DOI: | 10.1140/epjc/s10052-023-11509-8 |
| URL الوصول: | https://doaj.org/article/2c3476f2d76e4733b0f8695e66ffb3b7 |
| رقم الأكسشن: | edsdoj.2c3476f2d76e4733b0f8695e66ffb3b7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 14346052 |
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| DOI: | 10.1140/epjc/s10052-023-11509-8 |