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Frontiers of physics 2020年第4期15卷 133-151页

作者： yong-jia wang Qing-Feng Li School of Science Huzhou UniversityHuzhou 313000China Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China

The equation of state(EOS)of nuclear matter,i.e.,the thermodynamic relationship between the binding energy per nucleon,temperature,density,as well as the isospin asymmetry,has been a hot topic in nuclear physics and astrophysics for a long *** knowledge of the nuclear EOS is essential for studying the propertics of muclei,the structure of neutron stars,the dynamics of heavy ion collision(HIC),as well as neutron star *** offers a unique way to create muclear matter with high density and isospin asymmetry in terrestrial laboratory,but the formed dense nuclear matter exists only for a very short period,one cannot measure the nuclear EOS directly in ***,transport models which often incorporate phenomenological potentials as an input are utilized to deduce the EOS from the comparison with the observables mcasured in *** ultrarelativistic quantum molecular dynamics(UrQMD)model has been widely employed for investigating HIC from the Fermi energy(40 MeV per nucleon)up to the CERN Large Hadron Collider energies(TeV).With further improvement in the nmuclear mean-field potential term,the collision term,and the cluster recognition term of the UrQMD model,the newly measured collective flow and nuclear stopping data of light charged particles by the FOPI Collaboration can be *** this article we highlight our recent results on the studies of the muclear EOS and the muclear symmetry energy with the UrQMD *** opportunities and challenges in the extraction of the nuclear EOS from transport models and HIC experiments are discussed.

关键词： muclear equation of state symmetry energy heavy ion collision transport model

An explanation of the elliptic flow difference between proton and anti-proton from the UrQMD model with hadron potentials

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Science China(Physics,Mechanics & Astronomy) 2016年第3期59卷 60-65页

作者： Qing Feng Li yong jia wang Xiao Bao wang Cai Wan Shen School of Science Huzhou University

The time evolution of both proton and anti-proton v2 flows from Au＋Au collisions at √SNN=7.7 GeV are examined by using both pure cascade and mean-field potential versions of the UrQMD model. Due to a stronger repulsion at the early stage introduced by the repulsive potentials and hence much less annihilation probabilities, anti-protons are frozen out earlier with smaller v2 values. Therefore, the experimental data of anti-proton v2 as well as the flow difference between proton and anti-proton can be reasonably described with the potential version of UrQMD.

关键词： elliptic flow difference UrQMD mean field potential annihilation process

Helium-3 production from Pb+Pb collisions at SPS energies with the UrQMD model and the traditional coalescence afterburner

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Science China(Physics,Mechanics & Astronomy) 2016年第3期59卷 66-69页

作者： Qing Feng Li yong jia wang Xiao Bao wang Cai Wan Shen School of Science Huzhou University

A potential version of the UrQMD （UrQMD/M） transport model and a traditional coalescence model are combined to calculate the production of 3He fragments in central Pb＋Pb collisions at SPS energies 20-80 GeV/nucleon. It is found that the Lorentz transformation in the afterburner influences visibly the 3He yield and should be considered in calculations. The rapidity distribution of 3He multiplicities （including the concave shape） can be described well with UrQMD/M when it stops during tout=（100＋25） fm/c and the coalescence afterburner with one parameter set of （R0,P0）=（3.8 fm, 0.3 GeV/c） is taken into use afterwards.

关键词： rapidity distribution of Helium-3 UrQMD model coalescence model Lorentz transformation

Rapidity distribution of protons from the potential version of Ur QMD model and the traditional coalescence afterburner

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Science China(Physics,Mechanics & Astronomy) 2016年第2期59卷 36-39页

作者： Qing Feng Li yong jia wang Xiao Bao wang Cai Wan Shen School of Science Huzhou University

Rapidity distributions of both E895 proton data at AGS energies and NA49 net proton data at SPS energies can be described reasonably well with a potential version of the Ur QMD in which mean-field potentials for both pre-formed hadrons and confined baryons are considered, with the help of a traditional coalescence afterburner in which one parameter set for both relative distance R_0 and relative momentum P_0,(3.8 fm, 0.3 Ge V/c), is used. Because of the large cancellation between the expansion in R_0 and the shrinkage in P_0 through the Lorentz transformation, the relativistic effect in clusters has little effect on the rapidity distribution of free(net) protons. Using a Woods-Saxon-like function instead of a pure logarithmic function as seen by FOPI collaboration at SIS energies, one can fit well both the data at SIS energies and the Ur QMD calculation results at AGS and SPS energies. Further, it is found that for central Au+Au or Pb+Pb collisions at top SIS, SPS and RHIC energies, the proton fractions in clusters are about33%, 10%, and 0.7%, respectively.

关键词： rapidity distribution of protons UrQMD model coalescence model Lorentz transformation

Effects of sequential decay on collective flows and nuclear stopping power in heavy-ion collisions at intermediate energies

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Nuclear Science and Techniques 2023年第4期34卷 175-184页

作者： Kui Xiao Peng-Cheng Li yong-jia wang Fu-Hu Liu Qing-Feng Li State Key Laboratory of Quantum Optics and Quantum Optics Devices and Collaborative Innovation Center of Extreme Optics Institute of Theoretical PhysicsShanxi UniversityTaiyuan 030006China School of Science Huzhou UniversityHuzhou 313000China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China Institut für Theoretische Physik Goethe Universität FrankfurtFrankfurt am Main 60438Germany Institute of Modern Physics Chinese Academy of ScienceLanzhou 730000China

In this study, the rapidity distribution, collective flows, and nuclear stopping power in ^(197)Au+^(197)Au collisions at intermediate energies were investigated using the ultrarelativistic quantum molecular dynamics(UrQMD) model with GEMINI++ code. The UrQMD model was adopted to simulate the dynamic evolution of heavy-ion collisions, whereas the GEMINI++ code was used to simulate the decay of primary fragments produced by UrQMD. The calculated results were compared with the INDRA and FOPI experimental data. It was found that the rapidity distribution, collective flows, and nuclear stopping power were affected to a certain extent by the decay of primary fragments, especially at lower beam energies. Furthermore, the experimental data of the collective flows and nuclear stopping power at the investigated beam energies were better reproduced when the sequential decay effect was included.

关键词： Heavy-ion collisions Sequential decay effect Collective flow Nuclear stopping power

Collective flow and nuclear stopping in heavy ion collisions in Fermi energy domain

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Nuclear Science and Techniques 2018年第12期29卷 54-61页

作者： Peng-Cheng Li yong-jia wang Qing-Feng Li Hong-Fei Zhang School of Nuclear Science and Technology Lanzhou University School of Science Huzhou University Institute of Modern Physics Chinese Academy of Sciences

The effects of the in-medium nucleon-nucleon(NN) elastic cross section on the observables in heavy ion collisions in the Fermi energy domain are investigated within the framework of the ultrarelativistic quantum molecular dynamics model. The results simulated using medium correction factors of F=σ_(NN)^(in-medium)/σ_(NN)^(free)=0.2,0.3,0.5,and the density-and momentum-dependent factor obtained from the FU3 FP1 parametrization are compared with the FOPI and INDRA experimental data. It is found that the calculations using the correction factors F=0.2 and 0.5 reproduce the experimental data(i.e.,collective flow and nuclear stopping) at 40 and 150 MeV/nucleon, respectively. Calculations with the FU3 FP1 parametrization can best fit these experimental data. These conclusions can be confirmed in both^(197)Au+^(197)Au and^(129)Xe+^(120)Sn.

关键词：重离子碰撞原子精力流动集体修正因素动力学模型试验性

Machine learning the nuclear mass

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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

Probing nucleon effective mass splitting with light particle emission

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Nuclear Science and Techniques 2023年第6期34卷 135-146页

作者： Fang-Yuan wang Jun-Ping Yang Xiang Chen Ying Cui yong-jia wang Zhi-Gang Xiao Zhu-Xia Li Ying-Xun Zhang China Institute of Atomic Energy Beijing102413China School of Science Huzhou UniversityHuzhou313000China Department of Physics Tsinghua UniversityBeijing100084China Guangxi Key Laboratory of Nuclear Physics and Technology Guangxi Normal UniversityGuilin541004China

The main objective of this study was to investigate the impact of effective mass splitting on heavy-ion-collision *** first analyzed correlations between different nuclear matter parameters obtained from 119 effective Skyrme interaction *** values of the correlation coefficients illustrate that the magnitude of effective mass splitting is crucial for tight constraints on the symmetry energy via heavy-ion ***^(86)Kr+^(208)Pb system at beam energies ranging from 25 to 200A MeV was simulated within the framework of the improved quantum molecular dynamics model(ImQMD-Sky).Our calculations show that the slopes of the spectra of ln[Y(n)/Y(p)]and ln[Y(t)/Y(^(3)He)],which are the logarithms of the neutron to proton and triton to helium-3 yield ratios,are directly related to effective mass splitting and can be used to probe the effective mass splitting.

关键词： Effective mass splitting Symmetry energy Heavy-ion collisions Skyrme interaction

Unveiling the Re,Cr,and I diffusion in saturated compacted bentonite using machine-learning methods

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Nuclear Science and Techniques 2024年第6期35卷 65-77页

作者： Zheng-Ye Feng Jun-Lei Tian Tao Wu Guo-Jun Wei Zhi-Long Li Xiao-Qiong Shi yong-jia wang Qing-Feng Li Department of Engineering Huzhou UniversityHuzhou 313000China Institute of Theoretical Physics Shanxi UniversityTaiyuan 030006China College of Physics Science and Technology Shenyang Normal UniversityShenyang 110034China

The safety assessment of high-level radioactive waste repositories requires a high predictive accuracy for radionuclide diffusion and a comprehensive understanding of the diffusion *** this study,a through-diffusion method and six machine-learning methods were employed to investigate the diffusion of ReO_(4)^(−),HCrO_(4)^(−),and I−in saturated compacted bentonite under different salinities and compacted dry *** machine-learning models were trained using two *** dataset contained six input features and 293 instances obtained from the diffusion database system of the Japan Atomic Energy Agency(JAEA-DDB)and 15 *** other dataset,comprising 15,000 pseudo-instances,was produced using a multi-porosity model and contained eight input *** results indicate that the former dataset yielded a higher predictive accuracy than the *** gradient-boosting exhibited a higher prediction accuracy(R2=0.92)and lower error(MSE=0.01)than the other machine-learning *** addition,Shapley Additive Explanations,Feature Importance,and Partial Dependence Plot analysis results indicate that the rock capacity factor and compacted dry density had the two most significant effects on predicting the effective diffusion coefficient,thereby offering valuable insights.

关键词： Machine learning Effective diffusion coefficient Through-diffusion experiment Multi-porosity model Global analysis

Correction to:Machine learning the nuclear mass

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Nuclear Science and Techniques 2021年第11期32卷 13-14页

Following publication of the original article,Formula(2)is missing and Fig.11,Fig.9 are *** original article has been corrected and the Publisher apologized to the authors and the readers for the inconve-nience caused... 详细信息

关键词： identical corrected mass