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

• 作者: Ziyi Yuan Dong Bai Zhongzhou Ren Zhen Wang
• 来源期刊: 中国物理C（英文版）
• 年，卷（期）: 2022,（2）
• 所属期刊栏目: NUCLEAR PHYSICS
• 页码范围: 79-89
• 页数: 11页
• 语种: 英文
• DOI: 10.1088/1674-1137/ac321c

摘要

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 ob-served 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 al-gorithms have been applied successfully in different branches of modern physics.It is interesting to explore their ap-plicability 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 214U.The theoretical results show good agreement with the latest ex-perimental 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.