Abstract
Nuclear stopping in central Au+Au collisions at relativistic heavy-ion collider (RHIC) energies is studied in the framework of a cascade mode and the modified ultrarelativistic quantum molecular dynamics (UrQMD) transport model. In the modified mode, the mean field potentials of both formed and “preformed” hadrons (from string fragmentation) are considered. It is found that the nuclear stopping is increasingly influenced by the mean-field potentials in the projectile and target regions with the increase of the reaction energy. In the central region, the calculations of the cascade model considering the modifying factor can describe the experimental data of the PHOBOS collaboration.
Highlights
The study of strongly interacting matter at extreme temperatures and densities is provided a chance by heavy-ion collisions at ultrarelativistic energies [1,2,3,4,5]
This goal can be achieved by studying the pseudorapidity distribution of charged particles from central Au+Au collisions at relativistic heavy ion collider (RHIC) energies, within a transport model, the ultrarelativistic quantum molecular dynamics (UrQMD) model
At RHIC energies, as the pseudorapidity distribution of charged particles in the transverse direction has not been provided by experimental physicists, we study the nuclear stopping with the longitudinal pseudorapidity distribution
Summary
The study of strongly interacting matter at extreme temperatures and densities is provided a chance by heavy-ion collisions at ultrarelativistic energies [1,2,3,4,5]. The main purpose of this work is to extract the information on nuclear stopping by comparison of the pseudorapidity distributions of charged particles from a transport-model simulation with data. This goal can be achieved by studying the pseudorapidity distribution of charged particles from central Au+Au collisions at relativistic heavy ion collider (RHIC) energies, within a transport model, the ultrarelativistic quantum molecular dynamics (UrQMD) model. The modifying factor is considered in this cascade mode. This method is advantageous to directly compare existing data, and it can describe the experimental data very well
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