Theoretical predictions onα-decay properties of some unknown neutron-deficient actinide nuclei using machine learning

Abstract：

Neutron-deficient actinide nuclei provide a valuable window to probe heavy nuclear systems with large proton-neutron ratios. In recent years, several new neutron-deficient Uranium and Neptunium isotopes have been observed using α-decay spectroscopy [Z. Y. Zhang et al., Phys. Rev. Lett. 122, 192503 (2019); L. Ma et al., Phys. Rev. Lett. 125, 032502 (2020); Z. Y. Zhang et al., Phys. Rev. Lett. 126, 152502 (2021)]. In spite of these achievements, some neutron-deficient key nuclei in this mass region are still unknown in experiments. Machine learning algorithms have been applied successfully in different branches of modern physics. It is interesting to explore their applicability in α-decay studies. In this work, we propose a new model to predict the α-decay energies and half-lives within the framework based on a machine learning algorithm called the Gaussian process. We first calculate the α-decay properties of the new actinide nucleus $ {}^{214}{\rm{U}}$ . The theoretical results show good agreement with the latest experimental data, which demonstrates the reliability of our model. We further use the model to predict the α-decay properties of some unknown neutron-deficient actinide isotopes and compare the results with traditional models. The results may be useful for future synthesis and identification of these unknown isotopes.