Entropy per rapidity in Pb-Pb central collisions using Thermal and Artificial neural network(ANN) models at LHC energies

TL;DR

This paper calculates the entropy per rapidity in Pb-Pb collisions at LHC energies using experimental data and employs artificial neural networks to estimate and predict entropy, achieving good agreement with observed results.

Contribution

It introduces an ANN-based simulation approach to estimate entropy per rapidity in heavy-ion collisions, validated against experimental data at LHC energies.

Findings

ANN model accurately reproduces experimental entropy data

Mathematical equations describing entropy as a function of collision parameters

Successful extrapolation of transverse momentum spectra at zero momentum

Abstract

The entropy per rapidity $d S/d y$ produced in central Pb-Pb ultra-relativistic nuclear collisions at LHC energies is calculated using experimentally observed identified particle spectra and source radii estimated from Hanbury Brown-Twiss (HBT) for particles, $\pi$, $k$, $p$, $\Lambda$, $\Omega$, and $\bar{\Sigma}$, and $\pi$, $k$, $p$, $\Lambda$ and $K_s^0$ at $ \sqrt{s}$ $=2.76$ and $5.02$ TeV, respectively. Artificial neural network (ANN) simulation model is used to estimate the entropy per rapidity $d S/d y$ at the considered energies. The simulation results are compared with equivalent experimental data, and good agreement is achieved. A mathematical equation describes experimental data is obtained. Extrapolating the transverse momentum spectra at $p_T$ $=0$ is required to calculate $d S/d y$ thus we use two different fitting functions, Tsallis distribution and the Hadron Resonance Gas (HRG) model. The success of ANN model to describe the experimental measurements will imply further prediction for the entropy per rapidity in the absence of the experiment.