Studying Proton–Proton Interaction at Large Hadrons Collider Using Genetic Programming

Abstract

Abstract This paper describes how to use Genetic Programming (GP) as an evolutionary computational that is a family of algorithms for global optimization. GP, as a global optimization technique used by discovery of a new function for modeling physical phenomena. The p-p interactions are modeled at Large Hadron Collider (LHC) experiments, the number of charged particles multiplicity <n> and the total cross-section, σT, as functions of the total center of mass energy (from low to ultra-high energy), s are discovered by using GP. In view of the discovered function for 〈 n 〉 ( s ) , the overall trend of the values predicted is consistent with LHC data [predicted values are 34.8638 and 35.3520 at s = 13 T e V and s = 14 T e V respectively]. The new function σ T ( s ) , trained on experimental data of Particle Data Group (PDG) demonstrates a nice match to the other models. The predicted values of the total cross section at s = 13 T e V , and 14 TeV are found to be 109.0381 mb and 111.8329 mb respectively. Furthermore, the values predicted are agreed with other models like Block