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Akademický článek

Novel deep learning-based evaluation of neutron resonance cross sections

Autor: Ze-Hua Hu, Rui-Rui Xu, Dan-Hua Shang-Guan, Yang-Jun Ying, Heng Yong, Kang Xing, Xiao-Jun Sun

Publikováno v: Physics Letters B, Vol 857, Iss , Pp 138978- (2024)

Neutron resonance cross sections are essential in many nuclear science fields and applications. However, their evaluation and application are extremely complicated. Additionally, the high-frequency, super-wide spectral range of these cross sections c

Externí odkaz: https://doaj.org/article/0ddde5eb6ef44175abb565c4d2a7520c

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Akademický článek

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

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

Publikováno v: Physics Letters B, Vol 855, Iss , Pp 138825- (2024)

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 eval

Externí odkaz: https://doaj.org/article/7da0a873586f463f98f14d7b2c981231

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