2026-10-14T19:19:28Z https://www.scidb.cn/oai

10.57760/sciencedb.j00186.00656 2025-03-17T16:19:49Z

2025-03-17 Study of the nuclear mass model by sequential least squares programming doi:10.57760/sciencedb.j00186.00656 en v> 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. Nuclear mass model; binding energy; magic nuclei; sequential least squares algorithm whk PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset http://www.doi.org/https://doi.org/10.1007/s41365-025-01726-z Science Data Bank