Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei

Hui-Ling Wei; Xun Zhu; Chen Yuan

doi:10.1007/s41365-022-01096-w

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Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH | Updated：2022-10-24

- Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei
- Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei
- 核技术（英文版） 2022年33卷第9期文章编号：111
- Affiliations：
  
  1.College of Physics, Henan Normal University, Xinxiang 453007, China
- Author bio：
  
  htuwhling@126.com
- Funds：
  
  National Natural Science Foundation of China(11975091);Program for Innovative Research Team (in Science and Technology) in University of Henan Province, China(21IRTSTHN011)
- DOI：10.1007/s41365-022-01096-w
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Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei[J]. 核技术（英文版）, 2022,33(9):111

Hui-Ling Wei, Xun Zhu, Chen Yuan. Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei[J]. Nuclear Science and Techniques, 2022,33(9):111
Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei[J]. 核技术（英文版）, 2022,33(9):111 DOI： 10.1007/s41365-022-01096-w.

Hui-Ling Wei, Xun Zhu, Chen Yuan. Configurational information entropy analysis of fragment mass cross distributions to determine the neutron skin thickness of projectile nuclei[J]. Nuclear Science and Techniques, 2022,33(9):111 DOI： 10.1007/s41365-022-01096-w.

摘要

Abstract

Configurational information entropy (CIE) analysis has been shown to be applicable for determining the neutron skin thickness (,δ,np,) of neutron-rich nuclei from fragment production in projectile fragmentation reactions. The BNN + FRACS machine learning model was adopted to predict the fragment mass cross-sections (,σ,A,) of the projectile fragmentation reactions induced by calcium isotopes from ,36,Ca to ,56,Ca on a ,9,Be target at 140 MeV/u. The fast Fourier transform was adopted to decompose the possible information compositions in ,σ,A, distributions and determine the quantity of CIE (,S,A, [,f,]). It was found that the range of fragments significantly influences the quantity of ,S,A, [,f,], which results in different trends of ,S,A, [,f,] ~ ,δ,np, correlation. The linear ,S,A, [,f,] ~ ,δ,np, correlation in a previous study [Nucl. Sci. Tech. 33, 6 (2022)] could be reproduced using fragments with relatively large mass fragments, which verifies that ,S,A, [,f,] determined from fragment ,σ,A, is sensitive to the neutron skin thickness of neutron-rich isotopes.

关键词

Keywords

Neutron skin thicknessMass cross-section distributionConfigurational information entropyProjectile fragmentation reaction

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