• NUCLEAR TECHNIQUES
  • Vol. 46, Issue 9, 090202 (2023)
Xian GUAN1, Xing WEI1, Zikun LI1、2, Haijun FAN1, Jipeng ZHANG1, and Tao SUN1、*
Author Affiliations
  • 1State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China
  • 2Nuclear Technology Key Laboratory of Earth Science in Sichuan, Chengdu University of Technology, Chengdu 610059, China
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    DOI: 10.11889/j.0253-3219.2023.hjs.46.090202 Cite this Article
    Xian GUAN, Xing WEI, Zikun LI, Haijun FAN, Jipeng ZHANG, Tao SUN. Fast localization of radiation sources based on Support Vector Machine[J]. NUCLEAR TECHNIQUES, 2023, 46(9): 090202 Copy Citation Text show less

    Abstract

    Background

    Lost radioactive sources needs to be quickly retrieved, positioning of radioactive source in complex environment is the key to find the lost radioactive source. [Propose] This study aims to develope a novel approach for the rapid positioning of orphan sources using a NaI(Tl) array detection device.

    Method

    First of all, by leveraging the shadow effect between array detectors, a response curve between gamma-ray incidence angles and counts was obtained through the use of Monte Carlo simulation software. Then, the support vector machine (SVM) method was employed to establish a predictive mathematical model for the counting rate of array detectors as a function of gamma-ray incidence angle, utilizing. Finally, a radioactive source localization physical experiment platform was constructed, and a series of incidence angle response experiments were conducted for the validation of this approach applied to radioactive source localization under varying conditions.

    Results

    Eexperimental results demonstrate that, through the use of the SVM regression prediction model, the maximum average deviation of the angle is 9.21° whilst the minimum is 1.77° for the angle prediction of an orphan 137Cs point source.

    Conclusions

    This method can achieve rapid and accurate localization of an orphan radioactive source.

    Xian GUAN, Xing WEI, Zikun LI, Haijun FAN, Jipeng ZHANG, Tao SUN. Fast localization of radiation sources based on Support Vector Machine[J]. NUCLEAR TECHNIQUES, 2023, 46(9): 090202
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