Artificial neural network-based method for discriminating Compton scattering events in high-purity germaniumγ-ray spectrometer

To detect radioactive substances with low activity levels,an anticoincidence detector and a high-purity germanium(HPGe)detector are typically used simultaneously to suppress Compton scattering background,thereby resulting in an extremely low detection limit and improving the measurement accuracy.However,the complex and expensive hardware required does not facilitate the application or promotion of this method.Thus,a method is proposed in this study to discriminate the digital waveform of pulse signals output using an HPGe detector,whereby Compton scattering background is suppressed and a low minimum detectable activity(MDA)is achieved without using an expensive and complex anticoincidence detector and device.The electric-field-strength and energy-deposition distributions of the detector are simulated to determine the relationship between pulse shape and energy-deposition location,as well as the characteristics of energy-deposition distributions for fulland partial-energy deposition events.This relationship is used to develop a pulse-shape-discrimination algorithm based on an artificial neural network for pulse-feature identification.To accurately determine the relationship between the deposited energy of gamma(γ)rays in the detector and the 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 into the detector.Subsequently,the pulse signals are identified and classified to discriminate between partial-and full-energy deposition events.Some partial-energy deposition events are removed to suppress Compton scattering.The proposed method effectively decreases the MDA of an HPGeγ-energy dispersive spectrometer.Test results show that the Compton suppression factors for energy spectra obtained from measurements on ^(152)Eu,^(137)Cs,and ^(60)Co radioactive sources are 1.13(344 keV),1.11(662 keV),and 1.08(1332 keV),respectively,and that the corresponding MDAs are 1.4%,5.3%,and 21.6%lower,respectively.

Methods for obtaining characteristic γ-ray net peak count from interlaced overlap peak in HPGe γ-ray spectrometer system

For a characteristic c-ray with interlaced overlap peak, and the case where its reliable and credible net count cannot be obtained using the current high-purity germanium(HPGe) multichannel γ-ray spectrum software, two new methods are proposed herein to obtain the γ-ray net peak count from the interlaced overlap peak in the HPGe cray spectrometer system, of which one is the symmetric conversion method based on Gaussian distribution and the other is where the energy average value of two close γ-rays is regarded as the γ-ray energy. The experimental results indicate that the two methods mentioned above are reliable and credible. This study is significant for the development of better γ-ray spectrum processing software for measuring complex γ-ray spectra concerning the nuclear reaction cross section, neutron activation analysis, and analysis of transuranium elements, using an HPGe detector.

基于反符合测量技术的HPGeγ谱仪系统峰康比影响因素研究

介绍了反康普顿HPGeγ谱仪系统的结构组成和井型反符合工作原理,对系统在有无反符合测量条件、源与环探测器相对距离以及源强对峰康比的影响进行了实验研究。实验结果表明,系统在反符合测量条件下,标尺相对位置为46.5cm,源强较弱的情况时,反符合测量技术对系统具有很好的抑制康普顿坪的作用,系统峰康比可达最佳值。