Application of TENIS and artificial neural networks in neutron spectroscopy for BNCT beams in IRT-T and TRR: additional research; European Physical Journal Plus; Vol. 140
| Parent link: | European Physical Journal Plus.— .— New York: Springer Science+Business Media LLC. Vol. 140.— 2025.— Article number 893, 13 p. |
|---|---|
| その他の著者: | , , , , , |
| 要約: | Title screen Following the successful results of the ThErmal Neutron Imaging System (TENIS) for mono- and poly-energetic neutron sources, in this research, real-time data obtained from the TENIS were utilized for neutron spectroscopy at the exits of the Beam Shaping Assemblies (BSAs) located at the beam ports of Tomsk Polytechnic University Research Reactor (IRT-T) and Tehran Research Reactor (TRR). To achieve this purpose, 70-pixel thermal neutron images were generated for 109 mono-energetic neutrons, referred to as the neutron fluence response matrix, using the MCNP6.1 code. These images were used as the input of the artificial neural network (ANN) tools in MATLAB. Results indicated that the sigmoid transfer function in both hidden and output layers gives the best correlation between the predicted and actual spectra of Boron Neutron Capture Therapy (BNCT) beam lines in IRT-T and TRR, with correlation coefficients (R2) of 0.74 and 0.86, and root-mean-square error of 0.020 and 0.014, respectively (i.e., a max–min problem). The results suggest that the ANN-unfolded TENIS results can also accurately predict the energy spectrum of neutrons suitable for the BNCT Текстовый файл AM_Agreement |
| 言語: | 英語 |
| 出版事項: |
2025
|
| 主題: | |
| オンライン･アクセス: | https://doi.org/10.1140/epjp/s13360-025-06813-z |
| フォーマット: | 電子媒体 図書の章 |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=682672 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 682672 | ||
| 005 | 20251029113516.0 | ||
| 090 | |a 682672 | ||
| 100 | |a 20251029d2025 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a US | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a Application of TENIS and artificial neural networks in neutron spectroscopy for BNCT beams in IRT-T and TRR: additional research |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: 49 tit | ||
| 330 | |a Following the successful results of the ThErmal Neutron Imaging System (TENIS) for mono- and poly-energetic neutron sources, in this research, real-time data obtained from the TENIS were utilized for neutron spectroscopy at the exits of the Beam Shaping Assemblies (BSAs) located at the beam ports of Tomsk Polytechnic University Research Reactor (IRT-T) and Tehran Research Reactor (TRR). To achieve this purpose, 70-pixel thermal neutron images were generated for 109 mono-energetic neutrons, referred to as the neutron fluence response matrix, using the MCNP6.1 code. These images were used as the input of the artificial neural network (ANN) tools in MATLAB. Results indicated that the sigmoid transfer function in both hidden and output layers gives the best correlation between the predicted and actual spectra of Boron Neutron Capture Therapy (BNCT) beam lines in IRT-T and TRR, with correlation coefficients (R2) of 0.74 and 0.86, and root-mean-square error of 0.020 and 0.014, respectively (i.e., a max–min problem). The results suggest that the ANN-unfolded TENIS results can also accurately predict the energy spectrum of neutrons suitable for the BNCT | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t European Physical Journal Plus |c New York |n Springer Science+Business Media LLC. | |
| 463 | 1 | |t Vol. 140 |v Article number 893, 13 p. |d 2025 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 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 | |
| 701 | 1 | |a Ordonez |b C. H. |g Cesar Hueso | |
| 801 | 0 | |a RU |b 63413507 |c 20251029 | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u https://doi.org/10.1140/epjp/s13360-025-06813-z |z https://doi.org/10.1140/epjp/s13360-025-06813-z | |
| 942 | |c CF | ||