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首页|A method based on an artificial neural network for discriminating Compton scattering events in a high-purity germanium γ-ray spectrometer

A method based on an artificial neural network for discriminating Compton scattering events in a high-purity germanium γ-ray spectrometer

A method based on an artificial neural network for discriminating Compton scattering events in a high-purity germanium γ-ray spectrometer

中文摘要英文摘要

o detect radioactive substances with a low activity level, an anti-coincidence detector and high-purity germanium detector (HPGe) are often used in combination to suppress the Compton scattering background, thereby obtaining an extremely low detection limit and improving the measurement accuracy. However, the complex and expensive hardware system required does not facilitate application and promotion of this method. Thus, a method is proposed to discriminate the digital waveform of pulse signals output by a HPGe detector, whereby the Compton scattering background is suppressed and a low minimum detectable activity (MDA) is obtained without using an expensive and complex anti-coincidence detector and device. The electric field strength distribution and the energy deposition distribution in the detector are simulated to determine the relationship between the pulse shape and location of energy deposition, as well as the characteristics of the energy deposition distribution for full- and partial-energy deposition events. This relationship is used to develop a pulse shape discrimination (PSD) algorithm based on employing an artificial neural network (ANN) for pulse feature identification. To accurately determine the relationship between the deposited energy of gamma (g)-rays in the detector and deposition location, we extract four shape parameters from the pulse signals output by the detector. Machine learning is used to input the four shape parameters to the detector. Then, the pulse signals are identified and classified to discriminate between partial- and full-energy deposition events, and some partial-energy deposition events are removed to suppress Compton scattering. The proposed method effectively lowers the MDA of a HPGe γ-energy dispersive spectrometer. Test results show that the Compton suppression factors for energy spectra obtained from measurements on 152Eu, 137Cs and 60Co radioactive sources are 1.13 (344 keV), 1.11 (662 keV) and 1.08 (1332 keV), respectively, and the corresponding MDAs are lowered by 1.4%, 5.3% and 21.6%.

o detect radioactive substances with a low activity level, an anti-coincidence detector and high-purity germanium detector (HPGe) are often used in combination to suppress the Compton scattering background, thereby obtaining an extremely low detection limit and improving the measurement accuracy. However, the complex and expensive hardware system required does not facilitate application and promotion of this method. Thus, a method is proposed to discriminate the digital waveform of pulse signals output by a HPGe detector, whereby the Compton scattering background is suppressed and a low minimum detectable activity (MDA) is obtained without using an expensive and complex anti-coincidence detector and device. The electric field strength distribution and the energy deposition distribution in the detector are simulated to determine the relationship between the pulse shape and location of energy deposition, as well as the characteristics of the energy deposition distribution for full- and partial-energy deposition events. This relationship is used to develop a pulse shape discrimination (PSD) algorithm based on employing an artificial neural network (ANN) for pulse feature identification. To accurately determine the relationship between the deposited energy of gamma (g)-rays in the detector and deposition location, we extract four shape parameters from the pulse signals output by the detector. Machine learning is used to input the four shape parameters to the detector. Then, the pulse signals are identified and classified to discriminate between partial- and full-energy deposition events, and some partial-energy deposition events are removed to suppress Compton scattering. The proposed method effectively lowers the MDA of a HPGe γ-energy dispersive spectrometer. Test results show that the Compton suppression factors for energy spectra obtained from measurements on 152Eu, 137Cs and 60Co radioactive sources are 1.13 (344 keV), 1.11 (662 keV) and 1.08 (1332 keV), respectively, and the corresponding MDAs are lowered by 1.4%, 5.3% and 21.6%.

Jian Yang、Yang Hou、Song Qing、Chun-Di Fan、Lei Yan、Guo-Qiang Zeng、Hao-Wen Deng、Chuan-Hao Hu

10.12074/202312.00117V1

粒子探测技术、辐射探测技术、核仪器仪表

High-purity germanium γ-ray spectrometerPulse shape discriminationCompton scatteringArtificial neural networkMinimum detectable activity

High-purity germanium γ-ray spectrometerPulse shape discriminationCompton scatteringArtificial neural networkMinimum detectable activity

Jian Yang,Yang Hou,Song Qing,Chun-Di Fan,Lei Yan,Guo-Qiang Zeng,Hao-Wen Deng,Chuan-Hao Hu.A method based on an artificial neural network for discriminating Compton scattering events in a high-purity germanium γ-ray spectrometer[EB/OL].(2023-12-07)[2025-08-02].https://chinaxiv.org/abs/202312.00117.点此复制

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