Determination of the full energy peak efficiency of HPGe detector for varying soil compositions and Marinelli beaker dimensions using Artificial Neural Networks

Document Type

Article

Publication Date

2022

Abstract

Natural radioactivity measurement is studying the radiation emitted naturally from any nature’s components such as air, soil, water, etc. This field of study plays an important role in public health and safety management. Scientists and engineers can calculate the radiation doses received by inhabitants of a certain area by measuring the natural radioactivity in the same area. One of the main issues facing the standard measurement procedure is determining the appropriate volume of the sample to be taken from the area under investigation. The volume of the investigated sample affects the detection efficiency of the HPGe detector due to the self-shielding effect. In this work we show how using artificial neural networks can help in choosing the proper dimensions of the Marinelli beaker to be used in the detection process. We used the various compositions of soil as a case study. Results showed that artificial neural networks were capable of finding the relation between soil composition, Marinelli beaker dimensions, and gamma energy as inputs and the HPGe detection efficiency as output. The developed model’s regression was 99% compared to the detection efficiency calculated using MCNP. This will help to have a better prediction of the detection efficiency of natural radioactivity measurements.

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