Speaker
Description
Numerical simulations of the (3+1)D hydrodynamic + hadronic transport hybrid model provide quantitative descriptions of the dynamics of relativistic heavy-ion collisions from a few GeV to a few TeV [1]. The net proton cumulants in the final state encode important information about the QCD phase structure. However, studying high-order cumulants of net proton fluctuations require more than millions of simulation events, which poses a big computational challenge. In this work, we develop a neural network to mimic the net baryon charge evolution in the full (3+1)D hybrid model. The trained neural network enables us to efficiently compute the net proton cumulants. Based on the trained neural network, we study the net proton cumulants from fluctuations of initial-state baryon stopping modeled by the 3D Monte-Carlo Glauber model at the RHIC Beam Energy Scan energies.
[1] C. Shen and B. Schenke, “Longitudinal dynamics and particle production in relativistic nuclear collisions,” Phys. Rev. C 105, no.6, 064905 (2022)
