Decay of superheavy nuclei based on the random forest algorithm

Abstract

How nuclides decay in the superheavy region is key information for investigating new elements beyond oganesson and the island of stability. The Random Forest algorithm is applied to study the competition between different decay modes in the superheavy region, including $\alpha$ decay, ${\beta }^{-}$ decay, ${\beta }^{+}$ decay, electron capture and spontaneous fission. The observed half-lives and dominant decay mode are well reproduced. The dominant decay mode of 96.9 % nuclei beyond ${}^{212}$Po is correctly described. $\alpha$ decay is predicted to be the dominant decay mode for isotopes in new elements $Z=119-122$, except for spontaneous fission in some even-even ones because of the odd-even staggering effect. The predicted half-lives show the existence of a long-lived spontaneous fission island at the southwest of ${}^{298}$Fl caused by the competition of nuclear deformation and Coulomb repulsion. More understanding of spontaneous fission, especially beyond ${}^{286}$Fl, is crucial to search for new elements and the island of stability.

References in corpus (27)

• Nuclear ground-state masses and deformations: FRDM(2012)
• A high-bias, low-variance introduction to Machine Learning for physicists
• Surface diffuseness correction in global mass formula
• Novel features of nuclear forces and shell evolution in exotic nuclei
• Nuclear Energy Density Optimization
• Evolution of shell structure in exotic nuclei
• Universal decay law in charged-particle emission and exotic cluster radioactivity
• Machine Learning in Nuclear Physics
• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach
• Microscopic mechanism of charged-particle radioactivity and generalization of the Geiger-Nuttall law
• Nuclear charge radii: Density functional theory meets Bayesian neural networks
• Bayesian Evaluation of Incomplete Fission Yields
• Shell evolution and nuclear forces
• Study of Charge Radii with Neural Networks
• Novel Bayesian neural network based approach for nuclear charge radii
• Ab-initio no-core Gamow shell model calculations of multi-neutron systems
• A new empirical formula for α-decay half-life and decay chains of Z=120 isotopes
• Optimizing multilayer Bayesian neural networks for evaluation of fission yields
• $\alpha$ Decay Half-life Estimation and Uncertainty Analysis
• Trees and Forests in Nuclear Physics
• Bayesian Data Fusion of Imperfect Fission Yields for Augmented Evaluations
• Extended R-matrix description of two-proton radioactivity
• Competition between weak and alpha-decay modes in superheavy nuclei
• Self-consistent calculations of electron-capture decays in Z=118, 119, and 120 superheavy isotopes
• Solving Schrodinger equations using physically constrained neural network
• Shell-model study on properties of proton dripline nuclides with Z, N = 30-50 including uncertainty analysis