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.

arXiv comment8 pages, 3 figures, 1 table

References in corpus (35)

• Machine learning and the physical sciences
• Nuclear ground-state masses and deformations: FRDM(2012)
• Surface diffuseness correction in global mass formula
• Skyrme-Hartree-Fock-Bogoliubov nuclear mass formulas: Crossing the 0.6 MeV threshold with microscopically deduced pairing
• The impact of individual nuclear properties on $r$-process nucleosynthesis
• Machine Learning in Nuclear Physics
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Masses, Deformations and Charge Radii--Nuclear Ground-State Properties in the Relativistic Mean Field Model
• The limits of the nuclear landscape explored by the relativistic continuum Hatree-Bogoliubov theory
• Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach
• Bayesian approach to model-based extrapolation of nuclear observables
• Neutron drip line in the Ca region from Bayesian model averaging
• Nuclear mass predictions with radial basis function approach
• Mapping the proton drip-line up to A=70
• Nuclear landscape in covariant density functional theory
• Machine learning the nuclear mass
• Measurement of the Coherent Elastic Neutrino-Nucleus Scattering Cross Section on CsI by COHERENT
• Nuclear mass predictions with machine learning reaching the accuracy required by $r$-process studies
• Exploring QCD matter in extreme conditions with Machine Learning
• Principal component analysis of event-by-event fluctuations
• A Minimal Nuclear Energy Density Functional
• Deformed relativistic Hartree-Bogoliubov theory in continuum with a point-coupling functional. II. Examples of odd Nd isotopes
• Radial basis function approach in nuclear mass predictions
• 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
• Nuclear energy density functionals from machine learning
• High precision nuclear mass predictions towards a hundred kilo-electron-volt accuracy
• Phase Transition Study meets Machine Learning
• Statistical aspects of nuclear mass models
• 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
• Uncertainty quantification of an empirical shell-model interaction using principal component analysis
• High-precision nuclear chronometer for the cosmos
• Calibration of Energy Density Functionals with Deformed Nuclei
• Statistical learnability of 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