ANN-driven unfolding of epithermal neutron spectra in variable-geometry beam-shaping assemblies; Radiation Physics and Chemistry; Vol. 232

ANN-driven unfolding of epithermal neutron spectra in variable-geometry beam-shaping assemblies; Radiation Physics and Chemistry; Vol. 232

Chi tiết về thư mục
Parent link:	Radiation Physics and Chemistry.— .— Amsterdam: Elsevier Science Publishing Company Inc. Vol. 232.— 2025.— Article number 112666, 8 p.
Tác giả khác:	Bedenko S. V. Sergey Vladimirovich, Polozkov S. D. Sergey Dmitrievich, Vlaskin G. N. Gennadiy Nikolaevich, Ghal-Eh Nima, Rahmani F. Faezeh, Veretennikov D. G. Dmitry Gennadjevich, Bagherzadeh-Atashchi S. Somayyeh
Tóm tắt:	Title screen To study the risks associated with neutron fields from various sources, direct measurements using advanced detection technologies are essential. This study explores the Thermal Neutron Imaging System (TENIS) for characterizing the energy spectrum of an (α, n) neutron beam generated in the collimated channel of the Prizm-AN facility utilizing an IBN-10 source. We present the performance characteristics of the neutron beam, the permissible operational duration near the installation, a comprehensive map of thermal neutron flux parameters, and the results of spectrum unfolding achieved using artificial neural networks (ANNs). Our analysis confirms that TENIS can effectively measure the energy spectrum of slowed-down (α, n) neutrons produced under horizontal collimation. Additionally, we highlight the similarities between 2,3H fusion and (α, n) reactions, integrating the Prizm-AN complex with TENIS as a powerful tool for the real-time monitoring and control of plasma parameters within the paired plasma source neutrons (PSN) and variable-geometry beam-shaping assembly (vBSA) system Текстовый файл AM_Agreement
Ngôn ngữ:	Tiếng Anh
Được phát hành:	2025
Những chủ đề:	электронный ресурс труды учёных ТПУ Fast (α, n) neutron beams 2,3H fusion and (α, n) reactions Variable-geometry beam-shaping assembly Unfolding of the neutron spectrum Artificial neural networks
Truy cập trực tuyến:	https://doi.org/10.1016/j.radphyschem.2025.112666
Định dạng:	Điện tử Chương của sách
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679722

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