Turkish Journal of Physics




The investigation and analysis of the correlation between the mean transverse momentum () of charged particles and the charged particle multiplicity ($N_{ch}$) allow physicists to understand the contribution of multiple-parton 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 -$N_{ch}$ 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 and $N_{ch}$ in wide energy range $s^{1/2}$. In the framework of the proposed GEP 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 accuracy of the calculated and predicted results is assessed by comparing them with the available experimental data and the theoretical ones.


Modeling and simulation, pp(bar) and pp collisions, charged multiplicity, transverse momentum, correlations, symbolic regression, function mining

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