Study of the nuclear mass model by sequential least squares programming

Nuclear mass is an important property in both nuclear and astrophysics. In this study, we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms. The sequential least squares programming (SLSQP) algorithm augments the precision of this multinomial ma...

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Bibliographic Details
Published in:Nuclear science and techniques Vol. 36; no. 7; p. 129
Main Authors: Yang, Hang, Chen, Cun-Yu, Xu, Xiao-Yu, Wang, Han-Kui, Wang, You-Bao
Format: Journal Article
Language:English
Published: Shanghai Springer Nature B.V 01.07.2025
Subjects:
Algorithms
Approximation
Energy
Error reduction
Least squares
Neutrons
Nuclear physics
Optimization
ISSN:1001-8042, 2210-3147
Online Access:Get full text
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Summary:Nuclear mass is an important property in both nuclear and astrophysics. In this study, we explore an improved mass model that incorporates a higher-order term of symmetry energy using algorithms. The sequential least squares programming (SLSQP) algorithm augments the precision of this multinomial mass model by reducing the error from 1.863 MeV to 1.631 MeV. These algorithms were further examined using 200 sample mass formulae derived from the δE term of the Eisospin mass model. The SLSQP method exhibited superior performance compared to the other algorithms in terms of errors and convergence speed. This algorithm is advantageous for handling large-scale multiparameter optimization tasks in nuclear physics.
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ISSN:1001-8042
2210-3147
DOI:10.1007/s41365-025-01726-z