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首页> 外文期刊>Physica Scripta: An International Journal for Experimental and Theoretical Physics >Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities
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Predictive power of theoretical modelling of the nuclear mean field: examples of improving predictive capacities

机译：核平变场理论建模的预测力：提高预测能力的例子

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摘要

We examine the effects of the parametric correlations on the predictive capacities of the theoretical modelling keeping in mind the nuclear structure applications. The main purpose of this work is to illustrate the method of establishing the presence and determining the form of parametric correlations within a model as well as an algorithm of elimination by substitution (see text) of parametric correlations. We examine the effects of the elimination of the parametric correlations on the stabilisation of the model predictions further and further away from the fitting zone. It follows that the choice of the physics case and the selection of the associated model are of secondary importance in this case. Under these circumstances we give priority to the relative simplicity of the underlying mathematical algorithm, provided the model is realistic. Following such criteria, we focus specifically on an important but relatively simple case of doubly magic spherical nuclei. To profit from the algorithmic simplicity we chose working with the phenomenological spherically symmetric Woods-Saxon mean-field. We employ two variants of the underlying Hamiltonian, the traditional one involving both the central and the spin orbit potential in the Woods-Saxon form and the more advanced version with the self-consistent density-dependent spin-orbit interaction. We compare the effects of eliminating of various types of correlations and discuss the improvement of the quality of predictions ('predictive power') under realistic parameter adjustment conditions.
机译：我们研究了参数相关性对核结构应用的理论模型预测能力的影响。本作作品的主要目的是说明在模型中建立和确定参数相关性的方法以及通过参数相关的替换（参见文本）的消除算法。我们研究消除参数相关性的效果进一步和远离拟合区的模型预测的稳定。在这种情况下，物理案例的选择和相关模型的选择是具有次要重要性的。在这种情况下，我们优先考虑基础数学算法的相对简单性，只要模型是现实的。在此类标准之后，我们专注于一个重要但相对简单的双魔球核。从算法简单起来，我们选择使用现象学球体对称的树林 - 撒克逊植物。我们雇用了底层汉密尔顿人的两种变体，传统的涉及树木 - 撒克逊形式的中央和自旋轨道潜力以及具有自我一致密度依赖性旋转轨道交互的更先进的版本。我们比较消除各种类型相关性的影响，并讨论在现实参数调整条件下提高预测质量（“预测力”）。

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