Neutron Spectrometry Using Artificial Neural Networks

HR Vega-Carrillo,VM Hernandez-Davila,E Manzanares-Acuna,GAM Sanchez, MPI de la Torre,R Barquero,F Palacios,RM Villafane, TA Arteaga, JMO Rodriguez

Radiation Measurements（2005）SCI 3区SCI 2区

Univ Autonoma Zacatecas | Unidad Académica de Estudios Nucleares | Hospital Universitario Rio Hortega | Depto. Física Teórica

Cited 70|Views3

Abstract

An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab® program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem.

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Key words

artificial neural networks,unfolding,MCNP

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