Particles Multiplicity Based on Rapidity in Landau and Artificial Neural Network(ANN) Models

TL;DR

This paper compares Landau hydrodynamical and ANN models in estimating particle multiplicity per rapidity in high-energy heavy-ion collisions, finding both models effectively reproduce experimental data across various energies and particles.

Contribution

It introduces a combined approach using Landau hydrodynamics and ANN models to accurately estimate particle multiplicity in heavy-ion collisions.

Findings

Landau model accurately reproduces multiplicity across all energies.

ANN model effectively estimates multiplicity for all particles.

Both models align well with experimental data.

Abstract

ANN model is used to estimate the multiplicity per rapidity for charged pions and kaons observed in various high-energy experiments from central Au+Au collisions with energies ranging from 2-200 GeV, and then compared to available experimental data, including RHIC-BRAHMS, and the future facilities at NICA and FAIR. We also used Landau hydrodynamical approach, which has a better describtion for the evolution of hot and dense matter produced in ultra-relativistic heavy-ion collisions. The approach is fitted to both results estimated from experiment and ANN simulation. We noticed that the Landau model accurately reproduces the entire range of multiplicity per rapidity for all created particles at all energies. Also ANN model can reproduce the multiplicity per rapidity very well for all considered particles.