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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]
Copyright of Physics Letters B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Phase shift deep neural network approach for studying resonance cross sections for the 235U(n,f) reaction.
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  Data: <searchLink fieldCode="AR" term="%22Xing%2C+Kang%22">Xing, Kang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Xiao-Jun%22">Sun, Xiao-Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sxj0212@gxnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Rui-Rui%22">Xu, Rui-Rui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xuruirui@ciae.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Fang-Lei%22">Zou, Fang-Lei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Ze-Hua%22">Hu, Ze-Hua</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Ji-Min%22">Wang, Ji-Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tao%2C+Xi%22">Tao, Xi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Xiao-Dong%22">Sun, Xiao-Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tian%2C+Yuan%22">Tian, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Niu%2C+Zhong-Ming%22">Niu, Zhong-Ming</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Physics+Letters+B%22">Physics Letters B</searchLink>. Aug2024, Vol. 855, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Neutron+resonance%22">Neutron resonance</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+physics%22">Nuclear physics</searchLink><br /><searchLink fieldCode="DE" term="%22Resonance%22">Resonance</searchLink><br /><searchLink fieldCode="DE" term="%22Nuclear+research%22">Nuclear research</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+resonance%22">Stochastic resonance</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Physics Letters B is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1016/j.physletb.2024.138825
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        Text: English
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    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Neutron resonance
        Type: general
      – SubjectFull: Nuclear physics
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      – SubjectFull: Resonance
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      – SubjectFull: Nuclear research
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      – SubjectFull: Stochastic resonance
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      – TitleFull: Phase shift deep neural network approach for studying resonance cross sections for the 235U(n,f) reaction.
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            – D: 01
              M: 08
              Text: Aug2024
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              Y: 2024
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