التفاصيل البيبلوغرافية
| العنوان: |
Phase shift deep neural network approach for studying resonance cross sections for the 235U(n,f) reaction |
| المؤلفون: |
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 |
| المصدر: |
Physics Letters B, Vol 855, Iss , Pp 138825- (2024) |
| بيانات النشر: |
Elsevier, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Physics |
| مصطلحات موضوعية: |
Physics, QC1-999 |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
0370-2693 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S0370269324003836; https://doaj.org/toc/0370-2693 |
| DOI: |
10.1016/j.physletb.2024.138825 |
| URL الوصول: |
https://doaj.org/article/7da0a873586f463f98f14d7b2c981231 |
| رقم الانضمام: |
edsdoj.7da0a873586f463f98f14d7b2c981231 |
| قاعدة البيانات: |
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