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Updated: Jul 16, 2025

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
Published on: April 28, 2022
Neutron spectroscopy with TENIS using an artificial neural network
S Bagherzadeh-Atashchi1, N Ghal-Eh1, F Rahmani2
1Department of Physics, Faculty of Science, Ferdowsi University of Mashhad, P.O. Box 91775-1436, Mashhad, Iran.
Abstract:
In this research, a ThErmal Neutron Imaging System (TENIS) consisting of two perpendicular sets of plastic scintillator arrays for boron neutron capture therapy (BNCT) application has been investigated in a completely different approach for neutron energy spectrum unfolding. TENIS provides a thermal neutron map based on the detection of 2.22 MeV gamma-rays resulting from 1H(nth, γ)2D reactions, but in the present study, the 70-pixel thermal neutron images have been used as input data for unfolding the energy spectrum of incident neutrons. Having generated the thermal neutron images for 109 incident mono-energetic neutrons, a 70 × 109 response matrix has been generated using the MCNPX2.6 code for feeding into the artificial neural network tools of MATLAB. The errors of the final results for mono-energetic neutron sources are less than 10% and the root mean square error (RMSE) for the unfolded neutron spectrum of 252Cf is about 0.01. The agreement of the unfolding results for mono-energetic and 252Cf neutron sources confirms the performance of the TENIS system as a neutron spectrometer.
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