Neutron spectroscopy with TENIS using an artificial neural network; Applied Radiation and Isotopes; Vol. 201
| Parent link: | Applied Radiation and Isotopes.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 201.— 2023.— Article number 111035, 8 p. |
|---|---|
| Andere auteurs: | , , , , |
| Samenvatting: | 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 |
| Taal: | Engels |
| Gepubliceerd in: |
2023
|
| Onderwerpen: | |
| Online toegang: | https://doi.org/10.1016/j.apradiso.2023.111035 |
| Formaat: | Elektronisch Hoofdstuk |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680248 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 680248 | ||
| 005 | 20250516113407.0 | ||
| 090 | |a 680248 | ||
| 100 | |a 20250516d2023 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a NL | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a Neutron spectroscopy with TENIS using an artificial neural network |f S. Bagherzadeh-Atashchi, N. Ghal-Eh, F. Rahmani [et al.] | |
| 203 | |a Текст |b визуальный |c электронный | ||
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 31 tit | ||
| 330 | |a 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 | ||
| 336 | |a Текстовый файл | ||
| 371 | |a AM_Agreement | ||
| 461 | 1 | |t Applied Radiation and Isotopes |c Amsterdam |n Elsevier Science Publishing Company Inc. | |
| 463 | 1 | |t Vol. 201 |v Article number 111035, 8 p. |d 2023 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a Plastic scintillator | |
| 610 | 1 | |a TENIS | |
| 610 | 1 | |a Neutron spectrum unfolding | |
| 610 | 1 | |a Artificial neural network | |
| 610 | 1 | |a BNCT | |
| 701 | 1 | |a Bagherzadeh-Atashchi |b S. |g Somayyeh | |
| 701 | 1 | |a Ghal-Eh |b N. |g Nima | |
| 701 | 1 | |a Rahmani |b F. |g Faezeh | |
| 701 | 1 | |a Izadi-Najafabadi |b R. |g Reza | |
| 701 | 1 | |a Bedenko |b S. V. |c physicist |c Associate Professor of Tomsk Polytechnic University, Candidate of physical and mathematical sciences |f 1980- |g Sergey Vladimirovich |9 15078 | |
| 801 | 0 | |a RU |b 63413507 |c 20250516 | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u https://doi.org/10.1016/j.apradiso.2023.111035 |z https://doi.org/10.1016/j.apradiso.2023.111035 | |
| 942 | |c CF | ||