Diffuseness effect and radial basis function network for optimizing α decay calculations  

作　　者：Na-Na Ma Xiao-Jun Bao Hong-Fei Zhang 马娜娜;包小军;张鸿飞(School of Nuclear Science and Technology,Lanzhou University,Lanzhou 730000,China;Department of Physics,Collaborative Innovation Center for Quantum Effects,and Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education,Hunan Normal University,Changsha 410081,China)

机构地区：[1]School of Nuclear Science and Technology,Lanzhou University,Lanzhou 730000,China [2]Department of Physics,Collaborative Innovation Center for Quantum Effects,and Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education,Hunan Normal University,Changsha 410081,China

出　　处：《Chinese Physics C》2021年第2期459-466,共8页中国物理C（英文版）

基　　金：Supported by the National Natural Science Foundation of China(11947229,11675223,11675066);the China Postdoctoral Science Foundation(2019M663853);the Fundamental Research Funds for the Central Universities(lzujbky-2017-ot04)and Feitian Scholar Project of Gansu province。

摘　　要：A radial basis function network(RBFN)approach is adopted for the first time to optimize the calculation of$\alpha$decay half-life in the generalized liquid drop model(GLDM),while concurrently incorporating the surface diffuseness effect.The calculations presented herein agree closely with the experimental half-lives for 68 superheavy nuclei(SHN),achieving a remarkable reduction of 40%in the root-mean-square(rms)deviations of half-lives.Furthermore,using the RBFN method,the half-lives for four SHN isotopes,252-288Rf,272-310Fl,286-316119,and 292-318120,are predicted using the improved GLDM with the diffuseness correction and the decay energies from WS4 and FRDM as inputs.Therefore,we conclude that the diffuseness effect should be embodied in the proximity energy.Moreover,increased application of neural network methods in nuclear reaction studies is encouraged.

关 键 词：αdecay radial basis function network the surface diffuseness effect 

分 类 号：O571.321[理学—粒子物理与原子核物理]