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

摘要: 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 full- and 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 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 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 that the corresponding MDAs are 1.4%, 5.3%, and 21.6% lower, respectively