Function mining for the < PT> -Nch correlations in pp and pp(bar) collisions based on symbolic regression

Abstract

The investigation and analysis of the correlation between the mean transverse momentum ( < PT >) of charged particles and the charged particle multiplicity ( N ch ) allow physicists to understand the contribution of multipleparton interactions to the particle production mechanism. A symbolic regression (SR) method, based on gene expression programming (GEP), is proposed for mining a function that describes the < PT > -N ch correlation in proton-proton and proton-antiproton (pp and pp(bar)) collisions at collision energies from IRS to LHC. The discovered function simulates and models the correlation between < PT > and N ch in wide energy range s 1/2 . In the framework of the proposed GEP model for < PT > −N ch correlation, the equation obtained describes the main features of the experimental data.Predictions for < PT > −N ch correlations at the future LHC collision energy of 14 TeV are obtained. The accuracy of the calculated and predicted results is assessed by comparing them with the available experimental data and the theoretical ones.