Over the past decades, the relativistic density functional theory has been greatly developed and widely applied to investigate a variety of nuclear phenomena. In this paper, we briefly review the concept of covariant density functional theory in nuclear physics with a few latest applications in describing nuclear ground-state and excitation properties as well as nuclear dynamics. Moreover, attempts to build a microscopic and universal density functional are also discussed in terms of the successful fully self-consistent relativistic Brueckner–Hartree–Fock calculations.

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Relativistic density functional theory in nuclear physics

The nuclear charge radius is a fundamental property of an atomic nucleus. In this article we study the predictive power of empirical relations for experimental nuclear charge radii of neighboring nuclei and predict the unknown charge radii of 1085 nuclei based on the experimental CR2013 database within an uncertainty of 0.03 fm.

Predictions of nuclear charge radii


 The machine learning models are constructed to predict the fragment production cross sections in projectile fragmentation (PF) reactions using the Bayesian neural network (BNN) techniques. The massive learning for the BNN models is based on the 6393 fragments from 53 measured projectile fragmentation reactions. A direct BNN model and a physical guiding BNN by FRACS parametrization (BNN + FRACS) model have been constructed to predict the fragment cross section in projectile fragmentation reactions. It is verified that the BNN and BNN + FRACS models can well reproduce the wide range of fragment production in PF reactions with incident energy from 40 MeV/u to 1 GeV/u, reaction systems of projectile nucleus from $^{40}$Ar to $^{208}$Pb and various target nucleus. The high precision of the BNN and BNN + FRACS models makes them applicable in the low production rate of extreme rare isotopes in the future PF reactions with large asymmetry of projectile nucleus in the main new generation of radioactive nuclear beam factories.

Precise Machine Learning Models for fragment production in Projectile Fragmentation Reactions by Bayesian Neural Networks

Multi-task learning on nuclear masses and separation energies with the kernel ridge regression

Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation (PF) reactions using Bayesian neural network (BNN) techniques. The massive learning for BNN models is based on 6393 fragments from 53 measured projectile fragmentation reactions. A direct BNN model and physical guiding BNN via FRACS parametrization (BNN + FRACS) model have been constructed to predict the fragment cross section in projectile fragmentation reactions. It is verified that the BNN and BNN + FRACS models can reproduce a wide range of fragment productions in PF reactions with incident energies from 40 MeV/u to 1 GeV/u, reaction systems with projectile nuclei from 40Ar to 208Pb, and various target nuclei. The high precision of the BNN and BNN + FRACS models makes them applicable for the low production rate of extremely rare isotopes in future PF reactions with large projectile nucleus asymmetry in the new generation of radioactive nuclear beam factories.

Precise machine learning models for fragment production in projectile fragmentation reactions using Bayesian neural networks

http://manuscript.elsevier.com/S0092640X22000018/pdf/S0092640X22000018.pdf

Xingzhi Wang

Papers

Nuclear mass table in deformed relativistic Hartree–Bogoliubov theory in continuum, I: Even–even nuclei