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- Title
基于卷积模型的核信号仿真.
- Authors
邵玮豪; 梁勇飞; 杨朝文; 左晶鑫; 宋云
- Abstract
Due to the advantages of high detection efficiency and low cost, NaI (Tl) detectors are often used in the fields of nuclear radiation energy spectrum measurement and nuclear radiation supervision. Due to the limitations of experimental conditions, it is often difficult for researchers to obtain the ideal experimental nuclear signal. Although the simulation signal of the detector can be used to achieve the research purpose, there are certain differences between the conventional detector single exponential or double exponential model and the actual nuclear signal model, and a more accurate mathematical model is needed to describe the detector output signal. Through the analysis of the detector signal formation process, the detector is regarded as a linear time-invariant system, and the total convolution of the responses of each part of the detector is the output kernel signal model. Through the established detector output signal convolution model, combined with the amplitude distribution law of the measured gamma source and the adjacent pulse time interval distribution law, a more accurate NaI (Tl) detector output signal can be provided for use in gamma energy research on spectral measurement algorithms. By adjusting the pulse interval time, the accumulation situation of the detector output signal under different counts can be simulated, so as to be used for the research of the accumulation signal reduction algorithm. After the comparison test with the gamma radiation source, the simulated signal is consistent with the output signal of the real detector, which not only prevents researchers from contacting the radioactive source and improves the safety of radiation protection, but also provides great convenience for the research of digital nuclear signal processing algorithm and energy spectrum algorithm.
- Subjects
GAMMA ray sources; DIGITAL signal processing; SIGNAL convolution; GAMMA rays; NUCLEAR energy; COINCIDENCE
- Publication
Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban, 2023, Vol 60, Issue 1, p1
- ISSN
0490-6756
- Publication type
Article
- DOI
10.19907/j.0490-6756.2023.014003