At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an ...At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.展开更多
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 ...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)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%.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(No.12075308)。
文摘At high count rates,pile-up events involving neutron and gamma signals result in inaccurate neutron counting and distortions in the energy spectrum.Additionally,a bipolar cusp-like pulse shaping algorithm based on an unfolding synthesis technique was proposed.This algorithm exhibits a narrow pulse shape,and the parallel design of the dual algorithms enables the recovery of pile-up signal amplitudes while preserving the distinct characteristics of neutron and gamma signals.The simplicity of the algorithm facilitates real-time neutron/gamma discrimination on an FPGA,allowing the energy spectra to be updated with each incoming signal.Furthermore,the algorithm can be readily tailored to various experimental conditions by adjusting the decay time constants.Multi-objective optimization reduces the need for manual parameter tuning by rapidly identifying the optimal parameters.Testing with a^(241)Am-Be neutron source and a NaIL scintillator yielded a figure of merit(FoM)value of 2.11 and produced a clear energy spectrum even at high count rates.
基金supported by the National Natural Science Foundation of China(Nos.12375297 and 12105144).
文摘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)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%.
