Research PaperNo Access

Particles multiplicity based on rapidity in Landau and artificial neural network (ANN) models

Physics Department, Faculty of Education, Ain Shams University, 11771, Roxy, Cairo, Egypt

Egyptian Center for Theoretical Physics (ECTP), Nile University, Juhayna Square Off 26th-July-Corridor, 12588 Giza, Egypt

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R. M. Abdel Rahman

Basic Sciences Department, Modern Academy for Engineering and Technology, 11571, Mokattam, Cairo, Egypt

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, and

Mahmoud Hanafy

Physics Department, Faculty of Science, Benha University, 13518, Benha, Egypt

E-mail Address: mahmoud.nasar@fsc.bu.edu.eg

Corresponding author.

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https://doi.org/10.1142/S0217751X22500026Cited by:6 (Source: Crossref)

Abstract

In this paper, artificial neural network (ANN) model is used to estimate the multiplicity per rapidity for charged pions and kaons observed in various high-energy experiments from central $Au + Au$ heavy-ion collisions with energies ranging from 2–200 GeV, and then compared to available experimental data, including RHIC-BRAHMS experiment, and covering the energy range of the future accelerator facilities at NICA and FAIR. We also used Landau hydrodynamical approach, which has a better description 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 noted 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 the considered particles. This encourages us to use ANN model to predict the multiplicity per rapidity for $k^{-}$ particles at energies $12.2$ and $17.3$ GeV.

Keywords:

PACS: 24.10.Nz, 25.75.−q, 05.70.−a

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