Principal components of nuclear mass models

TL;DR

This paper uses principal component analysis to extract and combine effects from various nuclear mass models, resulting in improved mass prediction accuracy.

Contribution

It introduces a novel application of PCA to nuclear mass models, enhancing predictive accuracy by integrating effects from multiple models.

Findings

Principal components effectively capture effects from different models.

Recombined principal components lead to more accurate nuclear mass predictions.

The approach demonstrates the benefit of integrating multiple models through PCA.

Abstract

The principal component analysis approach is employed to extract the principal components contained in nuclear mass models for the first time. The effects coming from different nuclear mass models are reintegrated and reorganized in the extracted principal components. These extracted principal components are recombined to build new mass models, which achieve better accuracy than the original theoretical mass models. This indicates that the effects contained in different mass models can work together to improve the nuclear mass predictions with the help of the principal component analysis approach.