| Autor: |
Kang Xing, Xiao-Jun Sun, Rui-Rui Xu, Fang-Lei Zou, Ze-Hua Hu, Ji-Min Wang, Xi Tao, Xiao-Dong Sun, Yuan Tian, Zhong-Ming Niu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Physics Letters B, Vol 855, Iss , Pp 138825- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
0370-2693 |
| DOI: |
10.1016/j.physletb.2024.138825 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect