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

作　　者：Ziyi Yuan Dong Bai Zhongzhou Ren Zhen Wang 袁子懿;柏栋;任中洲;王震(School of Physics Science and Engineering,Tongji University,Shanghai 200092,China;Key Laboratory of Advanced Micro-Structure Materials,Ministry of Education,Shanghai 200092,China)

机构地区：[1]School of Physics Science and Engineering,Tongji University,Shanghai 200092,China [2]Key Laboratory of Advanced Micro-Structure Materials,Ministry of Education,Shanghai 200092,China

出　　处：《Chinese Physics C》2022年第2期79-89,共11页中国物理C（英文版）

基　　金：Supported by the National Natural Science Foundation of China(12035011,11975167,11761161001,11565010,11961141003,11905103,11947211);the National Key R&D Program of China(2018YFA04044032016YFE0129300);the Science and Technology Development Fund of Macao(008/2017/AFJ);the Fundamental Research Funds for the Central Universities(22120210138);the China Postdoctoral Science Foundation(2019M660095,2020T130478)。

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 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 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.

关 键 词：alpha decay machine learning actinide nuclei 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O571[自动化与计算机技术—控制科学与工程]