6 papers
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…
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)-…
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…
Temperature dependence of the nucleon-nucleon inelastic cross section in an isospin-asymmetric nuclear medium
Manzi Nan, Pengcheng Li, Guojun Wei +3
The nucleon-nucleon () inelastic cross section plays an important role in constraining the nuclear equation of state at high baryon density and in describing the formation and…
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…