Nuclide identification of radioactive sources from gamma spectra using artificial neural networks

IF 2.8 3区物理与天体物理 Q3 CHEMISTRY, PHYSICAL

Radiation Physics and Chemistry Pub Date : 2025-03-13 DOI:10.1016/j.radphyschem.2025.112692

N.P. Barradas , A. Vieira , M. Felizardo , M. Matos

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引用次数: 0

Abstract

Gamma spectroscopy is commonly used to identify the radionuclides present in samples or materials, by using the existing knowledge on the gamma ray energies and intensities for each radionuclide. However, when dealing with samples where the composition, internal configuration and shielding materials are unknown, as is the case, for instance, in nuclear security applications, the task can become challenging. Furthermore, gamma detection systems in field applications often do not have the high resolution typical of controlled laboratory conditions. In this work, we apply artificial intelligence techniques for automated identification of radioactive sources from gamma spectra obtained with a LaBr₃(Ce) detector with 3.6 % resolution at 662 keV. Combinations of up to 10 sources in each spectrum were used to train and test the artificial neural network developed. We report on the results, which show effective nuclide identification of radioactive sources from gamma spectra using ANNs.

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伽马能谱通常用于利用现有的有关每种放射性核素的伽马射线能量和强度的知识来识别样品或材料中存在的放射性核素。然而，在处理成分、内部构造和屏蔽材料未知的样品时，例如在核安全应用中，这项任务就变得具有挑战性。此外，现场应用中的伽马检测系统通常不具备实验室控制条件下的高分辨率。在这项工作中，我们应用人工智能技术，从 662 千伏分辨率为 3.6 % 的 LaBr3（Ce）探测器获得的伽马能谱中自动识别放射源。每个光谱中最多有 10 个放射源的组合被用来训练和测试所开发的人工神经网络。我们报告的结果表明，利用人工神经网络可以从伽马能谱中有效识别放射源的核素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiation Physics and Chemistry 化学-核科学技术

CiteScore

5.60

自引率

17.20%

发文量

574

审稿时长

12 weeks

期刊介绍： Radiation Physics and Chemistry is a multidisciplinary journal that provides a medium for publication of substantial and original papers, reviews, and short communications which focus on research and developments involving ionizing radiation in radiation physics, radiation chemistry and radiation processing. The journal aims to publish papers with significance to an international audience, containing substantial novelty and scientific impact. The Editors reserve the rights to reject, with or without external review, papers that do not meet these criteria. This could include papers that are very similar to previous publications, only with changed target substrates, employed materials, analyzed sites and experimental methods, report results without presenting new insights and/or hypothesis testing, or do not focus on the radiation effects.