Neutron spectroscopy with TENIS using an artificial neural network

Neutron spectroscopy with TENIS using an artificial neural network

Bibliographic Details
Parent link:Applied Radiation and Isotopes.— .— Amsterdam: Elsevier Science Publishing Company Inc.
Vol. 201.— 2023.— Article number 111035, 8 p.
Other Authors: Bagherzadeh-Atashchi S. Somayyeh, Ghal-Eh N. Nima, Rahmani F. Faezeh, Izadi-Najafabadi R. Reza, Bedenko S. V. Sergey Vladimirovich
Summary:Title screen
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
Текстовый файл
AM_Agreement
Published: 2023
Subjects:
электронный ресурс
труды учёных ТПУ
Plastic scintillator
TENIS
Neutron spectrum unfolding
Artificial neural network
BNCT
Online Access:https://doi.org/10.1016/j.apradiso.2023.111035
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680248

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