256 citations · 1.5k across the 58 of their papers we have counts for
58 papers
Further exploration of the machine-learning-based nuclear mass table
Liu Yaqi, Li Zhilong, Wang Yongjia +1
The mass of the atomic nucleus, as one of the fundamental physical quantities of the atomic nucleus, plays an important role in understanding and researching the structure of the a…
Machine learning the impact parameter in heavy-ion collisions at = 4 and 11 GeV: a cross-check study with UrQMD, AMPT, and JAM
Xiaoqing Yue, Guojun Wei, Yongjia Wang +7
By generating heavy-ion collision data with the ultrarelativistic quantum molecular dynamics (UrQMD) model, a multiphase transport (AMPT) model, and the JAM model, the impact param…
Effects of the centrality determination method for the equation of state and nucleonic observables from Au+Au collisions at = 2.4 GeV
Xiaoqing Yue, Pengcheng Li, Yongjia Wang +2
Centrality determination remains one of the major sources of systematic uncertainty in intermediate-energy heavy-ion collision analyses, especially for probing the nuclear equation…
Probing the Three-dimension Emission Source and Neutron Skin via - Correlations in Heavy-Ion Collisions
Haojie Zhang, Junhuai Xu, Pengcheng Li +6
The Richardson-Lucy algorithm is applied to reconstruct the three-dimensional source function of identical pions from their two-particle correlation functions. The algorithm's perf…
Unlocking the initial neutron density distribution from the two-pion HBT correlation function in heavy-ion collisions
Pengcheng Li, Manzi Nan, Haojie Zhang +8
Revealing the neutron density distribution in the nucleus is one of the crucial tasks of nuclear physics. Within the framework of the ultrarelativistic quantum molecular dynamic mo…
Bayesian analysis of properties of nuclear matter with the FOPI experimental data
Guojun Wei, Manzi Nan, Pengcheng Li +4
Based on the ultra-relativistic quantum molecular dynamics (UrQMD) transport model, combined with experimental data of directed flow, elliptic flow, and nuclear stopping power meas…
A novel filtering method for generating desired density profiles of colliding nuclei
Xilong Xiang, Manzi Nan, Pengcheng Li +3
Accurate modeling of the density profile is essential for studying heavy-ion collisions (HICs) with a transport model. Within the framework of the quantum molecular dynamics (QMD)-…
Isospin-dependent in-medium nucleon-Delta elastic cross section
Manzi Nan, Pengcheng Li, Yongjia Wang +2
In heavy-ion collisions at intermediate energies, the production and propagation of particles are crucial to understanding the nuclear equation of state and inferring the prope…
Importance of physical information on the prediction of heavy-ion fusion cross section with machine learning
Zhilong Li, Zepeng Gao, Ling Liu +3
In this work, the Light Gradient Boosting Machine (LightGBM), which is a modern decision tree based machine-learning algorithm, is used to study the fusion cross section (CS) of he…
Studies of nuclear equation of state with the HIRFL-CSR external-target experiment
Dong Guo, Xionghong He, Pengcheng Li +16
The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of…
Machine learning transforms the inference of the nuclear equation of state
Yongjia Wang, Qingfeng Li
Our knowledge of the properties of dense nuclear matter is usually obtained indirectly via nuclear experiments, astrophysical observations, and nuclear theory calculations. Advanci…
Probing the nucleon effective mass splitting with the light particle emission
Fang-Yuan Wang, Jun-Ping Yang, Xiang Chen +5
In this work, we first analyze the correlations among different nuclear matter parameters which are obtained by the 119 effective Skyrme interaction sets. The values of the correla…
Impacts of momentum dependent interaction, symmetry energy and near-threshold cross sections on isospin sensitive flow and pion observables
Yangyang Liu, Yingxun Zhang, Junping Yang +3
Based on the ultra-relativistic quantum molecular dynamics (UrQMD) model, the impacts of momentum dependent interaction, symmetry energy and near-threshold cross section…
How Do Constraints of Nuclear Symmetry Energy Reconcile with Different Models?
Yingxun Zhang, Yangyang Liu, Yongjia Wang +2
By simultaneously describing the data of isospin sensitive nucleonic flow and pion observables, such as and , with ultra-relativistic quantum molecular dy…
Elliptic flow in heavy-ion collisions at intermediate energy: the role of impact parameter, mean field potential, and collision term
Bo Gao, Yongjia Wang, Zepeng Gao +1
Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reverse tracing nucleons that are finally emitted at mid-rapidity (|| < 0.1) in the entire reaction p…
Effects of Initial Density Fluctuations on Cumulants in Au + Au Collisions at = 7.7 GeV
Xiaoqing Yue, Yongjia Wang, Qingfeng Li +1
Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, the effect of initial density fluctuations on cumulants of the net-proton multiplicity distribution in Au + A…
Decoding the nuclear symmetry energy event-by-event in heavy-ion collisions with machine learning
Yongjia Wang, Zepeng Gao, Hongliang Lü +1
Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectat…
Accessing the in-medium effects on nucleon-nucleon elastic cross section with collective flows and nuclear stopping
Pengcheng Li, Yongjia Wang, Qingfeng Li +1
A systematic study of the in-medium correction factor () on nucleon-nucleon elastic cross section is performed within the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) m…
Transport Model Comparison Studies of Intermediate-Energy Heavy-Ion Collisions
Hermann Wolter, Maria Colonna, Dan Cozma +49
Transport models are the main method to obtain physics information from low to relativistic-energy heavy-ion collisions. The Transport Model Evaluation Project (TMEP) has been purs…
Applying machine learning to determine impact parameter in nuclear physics experiments
C. Y. Tsang, Yongjia Wang, M. B. Tsang +14
Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect det…
Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning
Yongjia Wang, Fupeng Li, Qingfeng Li +2
A deep convolutional neural network (CNN) is developed to study symmetry energy effects by learning the mapping between the symmetry energy and the two-dimensional…
Comparison of Heavy-Ion Transport Simulations: Mean-field Dynamics in a Box
Maria Colonna, Ying-Xun Zhang, Yong-Jia Wang +26
Within the transport model evaluation project (TMEP) of simulations for heavy-ion collisions, the mean-field response is examined here. Specifically, zero-sound propagation is cons…
Application of machine learning in the determination of impact parameter in the Sn+Sn system
Fupeng Li, Yongjia Wang, Zepeng Gao +5
Background: Sn+Sn collisions at the beam energy of 270 MeVnucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the…
Machine learning the nuclear mass
Zepeng Gao, Yongjia Wang, Hongliang Lü +3
Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z…