标题： 扩展法扬斯能量密度泛函：优化与分析 显示英文标题

标题： Extended Fayans energy density functional: optimization and analysis

摘要： 法因斯能量密度泛函（EDF）在核密度泛函理论中成功地描述了全局核性质（结合能、电荷半径，尤其是半径差异）。 在最近的一项研究中，监督机器学习方法被用于校准法因斯EDF。 基于这一经验，在本工作中，我们通过将不包含矢量同位旋配对项的13D模型与扩展的14D模型进行比较，探讨添加矢量同位旋配对项的影响，这些项负责不同的质子和中子配对场。 校准的核心是一个精心选择的异构实验观测数据集，代表球形偶偶核的基态性质。 为了量化校准数据集对模型参数的影响以及新项的重要性，我们对两种模型进行了先进的敏感性和相关性分析。 扩展到14D使模型的整体质量提高了约30%。 14D模型增强的自由度减少了模型参数之间的相关性并增强了敏感性。 显示英文摘要

摘要： The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even-even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model by about 30%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity.