محفوظ في:
| المؤلفون الرئيسيون: | , , , , , , |
|---|---|
| التنسيق: | Preprint |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://arxiv.org/abs/2209.12201 |
| الوسوم: |
إضافة وسم
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جدول المحتويات:
- 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.