Principal components of nuclear mass models

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.

8 pages, 3 figures, 1 table

References in corpus (9)

• Surface diffuseness correction in global mass formula
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Nuclear mass predictions with machine learning reaching the accuracy required by $r$-process studies
• Principal component analysis of event-by-event fluctuations
• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Nuclear masses in extended kernel ridge regression with odd-even effects
• Phase Transition Study meets Machine Learning
• Ground state properties and potential energy surfaces of ${}^{270}$Hs from multidimensionally-constrained relativistic mean field model
• Sensitivity study of \emph{r}-process abundances to nuclear masses

Cited by in corpus (3)

• Exploratory study on the masses of odd-$Z$ nuclei and $r$-process simulation based on the deformed relativistic Hartree-Bogoliubov theory in continuum
• Further exploration of the machine-learning-based nuclear mass table
• Principal Components of Nuclear Mass Model Residuals