Artificial Neural Network for Spectrum unfolding Bonner Sphere Data

The use of Bonner Sphere Spectrometer (BSS) is a well-established method of measuring the energy distribution of neutron emission sources. The purpose of this research is to apply the Generalized Regression Neural Network (GRNN), a kind of Artificial Neural Network (ANN), to predict the neutron spectrum using the count rate data from a BSS. The BSS system was simulated with the MCNP5 Monte-Carlo code to calculate the response to neutrons of different energies for each combination of thermal neutron detector and polyethylene sphere. One hundred and sixty-three different types of neutron spectra were then investigated. GRNN Training and testing was carried out in the MATLAB environment. In the GRNN testing, eight-one predicted spectra were obtained as outputs of the GRNN. Comparison with standard spectra shows that 97.5% of the prediction errors were controlled below 1%, indicating ANN could be used as an alternative with high accuracy in neutron spectrum unfolding methodologies. Advantages and further improvements of this technique are also discussed.