The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network wi...The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demonstrated successfully using the Monte Carlo data itself and experimental data obtained from the Am-Be neutron source and D-T neutron source.展开更多
The energy spreading of recorded ions is influenced by straggling,geometrical acceptance angles and detector energy resolution effects in neutron depth profiling(NDP)and a symmetric Gaussian function model was customa...The energy spreading of recorded ions is influenced by straggling,geometrical acceptance angles and detector energy resolution effects in neutron depth profiling(NDP)and a symmetric Gaussian function model was customarily applied before.In addition,the spectra of mono-energetic alpha particles show a well known asymmetric shape as well when measured by silicon detectors.This article presents a physical model predicting the observed energy spectrum of a sample ion with target nuclides in prearranged depths.It is expressed as the convolution of a Gaussian function with a left-hand double-exponential function.Experiment showed that the predicted ions spectrum derived from the asymmetric model matches the observed energy spectrum.Therefore,the model can be applied to produce matrix for inversion of NDP spectrum.展开更多
基金supported by the National Magnetic Confinement Fusion Science Program (Grant No. 2010GB111002)
文摘The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demonstrated successfully using the Monte Carlo data itself and experimental data obtained from the Am-Be neutron source and D-T neutron source.
文摘The energy spreading of recorded ions is influenced by straggling,geometrical acceptance angles and detector energy resolution effects in neutron depth profiling(NDP)and a symmetric Gaussian function model was customarily applied before.In addition,the spectra of mono-energetic alpha particles show a well known asymmetric shape as well when measured by silicon detectors.This article presents a physical model predicting the observed energy spectrum of a sample ion with target nuclides in prearranged depths.It is expressed as the convolution of a Gaussian function with a left-hand double-exponential function.Experiment showed that the predicted ions spectrum derived from the asymmetric model matches the observed energy spectrum.Therefore,the model can be applied to produce matrix for inversion of NDP spectrum.
