Neutron spectral unfolding using the Monte Carlo method

Neutron spectroscopy with TENIS using an artificial neural network

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.

Gamma-Ray Spectral Unfolding of CdZnTe-Based Detectors Using a Genetic Algorithm

The analysis of γ-ray spectra can be an arduous task, especially in the case of room temperature semiconductor detectors, where several distortions and instrumental artifacts conceal the true spectral shape. We developed a genetic algorithm to perform the unfolding of γ-spectra in order to restore the true energy distribution of the incoming radiation. We successfully validated our approach on experimental spectra of four radionuclides (241Am, 57Co, 137Cs and 133Ba) acquired with two CdZnTe-based detectors with different contact geometries (single pixel and drift strip). The unfolded spectra consist of δ-like peaks in correspondence with the radiation emissions of each radioisotope.