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<p>The pulse shape discrimination technique plays a pivotal role in neutron field measurements using organic scintillator detectors, and the particle-type labeling accuracy of the pulse waveform dataset has a significant impact on its performance, especially with the growing use of machine learning methods. In this study, a high-accuracy labeling method for pulse waveform datasets based on the time-of-flight (TOF) filtering method, an improved charge comparison method (CCM), and the coincidence measurement method is proposed. The relationship between the experimental parameters and the chance coincidence proportion in the TOF measurement was derived to reduce contamination from chance coincidences at the experimental level. Based on this, an experiment was conducted to obtain raw data using the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^{241}\text {AmBe}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mmultiscripts> <mrow /> <mrow /> <mn>241</mn> </mmultiscripts> <mtext>AmBe</mtext> </mrow> </math></EquationSource> </InlineEquation> source, and a piled-up identification algorithm based on reference waveform cross-correlation and differential analysis was designed to filter out piled-up pulses. To improve the labeling accuracy, the CCM was optimized, a simple method of selecting the TOF interval for a lower chance coincidence proportion was proposed, and a low-amplitude pulse waveform dataset construction method based on coincidence measurements was developed. To verify these methods, eight pulse waveform datasets were constructed using different combinations of the proposed approaches. Three neural network structures and a corresponding evaluation parameter were designed to test the quality of these datasets. The results showed that the particle identification performance of the CCM was significantly improved after optimization, with the neutron-to-gamma-ray misidentification rate reduced by more than 35%. The proposed accuracy improvement methods reduced ambiguous identification results from these artificial neural networks by more than 50%.</p>

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A high-accuracy particle-type labeling method for organic scintillator pulse waveform datasets

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Lin-Jun Hou,
  • Peng Xu,
  • Zhi-Meng Hu,
  • Jie Cheng

摘要

The pulse shape discrimination technique plays a pivotal role in neutron field measurements using organic scintillator detectors, and the particle-type labeling accuracy of the pulse waveform dataset has a significant impact on its performance, especially with the growing use of machine learning methods. In this study, a high-accuracy labeling method for pulse waveform datasets based on the time-of-flight (TOF) filtering method, an improved charge comparison method (CCM), and the coincidence measurement method is proposed. The relationship between the experimental parameters and the chance coincidence proportion in the TOF measurement was derived to reduce contamination from chance coincidences at the experimental level. Based on this, an experiment was conducted to obtain raw data using the \(^{241}\text {AmBe}\) 241 AmBe source, and a piled-up identification algorithm based on reference waveform cross-correlation and differential analysis was designed to filter out piled-up pulses. To improve the labeling accuracy, the CCM was optimized, a simple method of selecting the TOF interval for a lower chance coincidence proportion was proposed, and a low-amplitude pulse waveform dataset construction method based on coincidence measurements was developed. To verify these methods, eight pulse waveform datasets were constructed using different combinations of the proposed approaches. Three neural network structures and a corresponding evaluation parameter were designed to test the quality of these datasets. The results showed that the particle identification performance of the CCM was significantly improved after optimization, with the neutron-to-gamma-ray misidentification rate reduced by more than 35%. The proposed accuracy improvement methods reduced ambiguous identification results from these artificial neural networks by more than 50%.