https://doi.org/10.1140/epjp/s13360-025-06048-y
Regular Article
Enhanced gamma-ray spectrum transformation: NaI(Tl) scintillator to HPGe semiconductor via machine learning
1
Department of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran
2
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
a
hafarideh@aut.ac.ir
b
mitragh@skku.edu
Received:
22
January
2024
Accepted:
22
January
2025
Published online:
9
February
2025
Thallium-activated sodium iodide scintillation (NaI(Tl)) and high-purity germanium semiconductor (HPGe) detectors are two commonly employed gamma spectroscopy devices. NaI(Tl) detectors are preferred for their cost-effectiveness, efficiency, and ease of construction, while HPGe detectors have superior resolution but face challenges in temperature operation and they are expensive. This article investigates the application of machine learning algorithms, specifically K-Nearest Neighbors (KNN) and a Multi-Channel Output Regression based on Support Vector Regression (MCO-SVR), to enhance the performance of NaI(Tl) detectors by transforming its gamma spectrum into HPGe spectrum. The model was trained using datasets generated from a limited radioisotope library and demonstrated excellent performance across a diverse range of measured experimental test data. The evaluation included various scenarios, such as low-count spectra and background effects. The KNN model exhibited optimal performance, achieving an accuracy of 98.69% with a Manhattan distance metric. In contrast, the MCO-SVR model, employing both direct and chained approaches, exhibited varied results with different kernel types, with the polynomial kernel in the direct approach yielding the value 97.45% accuracy. Overall, the results indicate that machine learning algorithms have the potential to improve the performance of NaI(Tl) detectors and expand their applications in various fields of nuclear security.
© The Author(s) 2025
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