Function mining for the $-N_{ch}$ correlations in pp and pp(bar) collisions based on symbolic regression

Özet

The investigation and analysis of the correlation between the mean transverse momentum ( ) ofcharged particles and the charged particle multiplicity (Nch) allow physicists to understand the contribution of multipleparton interactions to the particle production mechanism. A symbolic regression (SR) method, based on gene expressionprogramming (GEP), is proposed for mining a function that describes the $-N_{ch}$ correlation in proton–proton andproton–antiproton (pp and pp(bar)) collisions at collision energies from IRS to LHC. The discovered function simulatesand models the correlation between and $N_{ch}$ in wide energy range $s^{1/2}$. In the framework of the proposedGEP model for $-N_{ch}$ correlation, the equation obtained describes the main features of the experimental data.Predictions for $-N_{ch}$ correlations at the future LHC collision energy of 14 TeV are obtained. The accuracyof the calculated and predicted results is assessed by comparing them with the available experimental data and thetheoretical ones.