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Nuclear Science and Techniques 2021年第10期32卷 88-100页

作者： Ze-Peng Gao Yong-Jia Wang Hong-Liang Lu Qing-Feng Li Cai-Wan Shen Ling Liu College of Physics Science and Technology Shenyang Normal UniversityShenyang 110034China School of Science Huzhou UniversityHuzhou 313000China HiSilicon Research Department Huawei Technologies Co.Ltd.Shenzhen 518000China Institute of Modern Physics Chinese Academy of ScienceLanzhou 730000China

Background:The masses of-2500 nuclei have been measured experimentally;however,>7000 isotopes are predicted to exist in the nuclear landscape from H(Z=1)to Og(Z=118)based on various theoretical *** the mass of the remaining isotopes is a popular topic in nuclear *** learning has served as a powerful tool for learning complex representations of big data in many ***:We use Light Gradient Boosting Machine(LightGBM),which is a highly efficient machine learning algorithm,to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear ***:Several characteristic quantities(e.g.,mass number and proton number)are fed into the LightGBM algorithm to mimic the patterns of the residual δ(Z,A)between the experimental binding energy and the theoret-ical one given by the liquid-drop model(LDM),Duflo–Zucker(DZ,also dubbed DZ28)mass model,finite-range droplet model(FRDM,also dubbed FRDM2012),as well as the Weizsacker–Skyrme(WS4)model to refine these mass ***:By using the experimental data of 80%of known nuclei as the training dataset,the root mean square devia-tions(RMSDs)between the predicted and the experimental binding energy of the remaining 20%are approximately 0.234±0.022,0.213±0.018,0.170±0.011,and 0.222±0.016 MeV for the LightGBM-refined LDM,DZ model,WS4 model,and FRDM,*** values are approximately 90%,65%,40%,and 60%smaller than those of the corresponding origin mass *** RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly *** one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical *** addition,the two-neutron separation energies of several newly measured nuclei(e.g.,some isotopes of Ca,Ti,Pm,and Sm)pre-dicted with LightGBM-refined mass models are also in good agreement with

关键词： Nuclear mass Machine learning Binding energy separation energy

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Properties of Z=114 super-heavy nuclei

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Nuclear Science and Techniques 2021年第6期32卷 1-10页

作者： Yu-Qi Xin Na-Na Ma Jun-Gang Deng Tian-Liang Zhao Hong-Fei Zhang School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Joint Department for Nuclear Physics Institute of Modern PhysicsCAS and Lanzhou UniversityLanzhou 730000China Engineering Research Center for Neutron Application Ministry of EducationLanzhou UniversityLanzhou 730000China

The center of the stability island of super-heavy nuclei(SHN)is the subject of intense experimental and theoretical investigations and has potential technological applications.^(298)^(114) Fl lies in the Z=114 isotopic chain as a persuasive candidate of the spherical double-magic nucleus in SHN,and in this study,the calculations of nuclear binding energies,one-nucleon and two-nucleon separation energies,a-decay energies,and the corresponding halflives provide strong evidence for this *** calculations within an improved Weizsacker-Skyrme nuclear mass model(WS*)were performed and compared with the calculations of the finite-range droplet model(FRDM2012)and experimental data for Z=114 isotopes and N=184 ***,the corresponding single-particle levels in a Woods-Saxon potential well with a spin-orbit term are calculated,which can be used as a powerful indicator to identify the shell effects existing *** the study of the properties of the isotopic chain and microphysical quantities provide a vital signal that ^(298)^(114) Fl is a spherical double-magic nucleus and also the center of the SHN.

关键词： Super-heavy nuclei separation energy Single-particle level αdecay

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