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Phase shift deep neural network approach for studying resonance cross sections for the 235U(n,f) reaction.

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Title: Phase shift deep neural network approach for studying resonance cross sections for the 235U(n,f) reaction.
Authors: Xing, Kang1,2 (AUTHOR), Sun, Xiao-Jun1,2 (AUTHOR) sxj0212@gxnu.edu.cn, Xu, Rui-Rui1 (AUTHOR) xuruirui@ciae.ac.cn, Zou, Fang-Lei2 (AUTHOR), Hu, Ze-Hua3 (AUTHOR), Wang, Ji-Min1 (AUTHOR), Tao, Xi1 (AUTHOR), Sun, Xiao-Dong1 (AUTHOR), Tian, Yuan1 (AUTHOR), Niu, Zhong-Ming4 (AUTHOR)
Source: Physics Letters B. Aug2024, Vol. 855, pN.PAG-N.PAG. 1p.
Subjects: Artificial neural networks, Neutron resonance, Nuclear physics, Resonance, Nuclear research, Stochastic resonance
Abstract: Due to the complex structures associated with neutron resonance cross sections, their accurate evaluation has received considerable attention in the field of nuclear data research. The traditional R-matrix method still faces some difficulties in evaluating the neutron resonance data, especially in briefly reproducing the high-frequency oscillating cross sections. Recently, the applications of machine learning methods in nuclear physics have been expanding. In this paper, a novel Phase Shift Deep Neural Network (PSDNN) method, which not only overcomes the limitations of other machine learning methods in fitting the high-frequency oscillating data, but also is more concise than the R-matrix method, is developed to reproduce the neutron resonance cross sections. The results show that PSDNN method can simultaneously reproduce the low and high-frequency oscillating cross sections for the 235U(n , f) reaction with high accuracy and efficiency. Moreover, from an algorithmic point of view, the PSDNN method lays a solid foundation for further fine-grained processing of experimental data and extraction of critical neutron resonance parameters, opening up new possibilities for practical applications in nuclear data research. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:Due to the complex structures associated with neutron resonance cross sections, their accurate evaluation has received considerable attention in the field of nuclear data research. The traditional R-matrix method still faces some difficulties in evaluating the neutron resonance data, especially in briefly reproducing the high-frequency oscillating cross sections. Recently, the applications of machine learning methods in nuclear physics have been expanding. In this paper, a novel Phase Shift Deep Neural Network (PSDNN) method, which not only overcomes the limitations of other machine learning methods in fitting the high-frequency oscillating data, but also is more concise than the R-matrix method, is developed to reproduce the neutron resonance cross sections. The results show that PSDNN method can simultaneously reproduce the low and high-frequency oscillating cross sections for the 235U(n , f) reaction with high accuracy and efficiency. Moreover, from an algorithmic point of view, the PSDNN method lays a solid foundation for further fine-grained processing of experimental data and extraction of critical neutron resonance parameters, opening up new possibilities for practical applications in nuclear data research. [ABSTRACT FROM AUTHOR]
ISSN:03702693
DOI:10.1016/j.physletb.2024.138825