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<p>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&#xa0;MeV to 1.631&#xa0;MeV. These algorithms were further examined using 200 sample mass formulae derived from the <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1726_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="25" /> </InlineMediaObject> <EquationSource Format="TEX">\(\delta E\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>δ</mi> <mi>E</mi> </mrow> </math></EquationSource> </InlineEquation> term of the <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1726_Article_IEq2.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="49" /> </InlineMediaObject> <EquationSource Format="TEX">\(E_\text {isospin}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>E</mi> <mtext>isospin</mtext> </msub> </math></EquationSource> </InlineEquation> 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.</p>

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Study of the nuclear mass model by sequential least squares programming

  • 期刊论文

  • 发表日期：

    2025年5月26日

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  • Hang Yang,
  • Cun-Yu Chen,
  • Xiao-Yu Xu,
  • Han-Kui Wang,
  • You-Bao Wang

摘要

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 \(\delta E\) δ E term of the \(E_\text {isospin}\) E isospin 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.