Determination of neutron spectrum based on artificial neural network using liquid scintillation detector EJ-301.

This paper focuses on the neutron spectrum measurement using a liquid scintillation detector, where the neutron spectrum could be identified and unfolded from the light output distribution of the EJ-301 liquid scintillation detector through a linear artificial neural network (ANN). The response functions of the EJ-301 detector for monoenergetic neutron sources, as well as the light outputs, have been simulated and calculated by Monte Carlo procedure FLUKA. The linear ANN was trained and tested through the simulated data, where response functions were set as the input of ANN and the corresponding neutron spectra were output. Therefore, the neutron spectrum-unfolding model was created. This spectrum-unfolding model was tested through the light outputs induced by monoenergetic neutrons and the random superposition of them. Unfolding results show that this model could identify the information of the neutron spectrum accurately from the light outputs of a liquid scintillation detector. Moreover, the EJ-301 detector was used to measure the radioactivity of 252Cf, and the pulse height distribution induced by neutrons was derived through the charge-comparison method to remove the influence of gamma rays. The measured pulse height distribution was unfolded by the trained model, and measured results show that the unfolded neutron spectrum of 252Cf was consistent with the reference one. This paper presents the feasibility that the unknown neutron spectrum could be identified and confirmed through a linear neural network trained by simulated monoenergetic neutron response functions, which could be a candidate of choice for the determination of the neutron spectrum.