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<p>To address the interference from non-radioactive components in the environment and achieve efficient identification of mixed nuclide spectra, we propose a novel isotope identification method, LSNet. Experimental results demonstrate that, with comparable parameter counts and computational complexity, LSNet outperforms models such as the Gate-Recurrent-Unit (GRU) across various evaluation metrics. Additionally, LSNet shows significantly higher accuracy in identifying anomalous nuclide spectra compared to other models. Finally, we demonstrate that LSNet effectively identifies mixed nuclides.</p>

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LSNet: efficient nuclide spectrum recognition network

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Pengzhang Yu,
  • Yingrui Hu,
  • Xu Wang,
  • Ying Cai,
  • Daji Ergu,
  • Yong Xu,
  • Shengbo Tan

摘要

To address the interference from non-radioactive components in the environment and achieve efficient identification of mixed nuclide spectra, we propose a novel isotope identification method, LSNet. Experimental results demonstrate that, with comparable parameter counts and computational complexity, LSNet outperforms models such as the Gate-Recurrent-Unit (GRU) across various evaluation metrics. Additionally, LSNet shows significantly higher accuracy in identifying anomalous nuclide spectra compared to other models. Finally, we demonstrate that LSNet effectively identifies mixed nuclides.