Neutron spectrometry using artificial neural networks for a Bonner sphere spectrometer with a 3He detector

Neutron spectra unfolding and dose equivalent calculation are complicated tasks in radiation protection, are highly dependent of the neutron energy, and a precise knowledge on neutron spectrometry is essential for all dosimetry-related studies as well as many nuclear physics experiments. In previous works have been reported neutron spectrometry and dosimetry results, by using the artificial neural networks (Ann) technology as alternative solution, starting from the count rates of a Bonner spheres system with a 6LiI(Eu) thermal neutrons detector, 7 polyethylene spheres and the UTA4 response matrix with 31 energy bins. In this work, an Ann was designed and optimized by using the RDAnn methodology for the Bonner spheres system used at CIEMAT Spain, which is composed of a 3He neutron detector, 12 moderator spheres and a response matrix for 72 energy bins. For the Ann design process a neutrons spectra catalogue compiled by the IAEA was used. From this compilation, the neutrons spectra were converted from lethargy to energy spectra. Then, the resulting energy fluence spectra were re-bin ned by using the MCNP code to the corresponding energy bins of the 3He response matrix before mentioned. With the response matrix and the re-bin ned spectra the counts rate of the Bonner spheres system were calculated and the resulting re-bin ned neutrons spectra and calculated counts rate were used as the Ann training data set. (Author)