Studying total proton-proton cross section collision at large hadron collider using gene expression programming

New technique is presented for modeling total cross section of proton-proton (p-p) collision from low to ultra-high energy regions using gene expression programming (GEP). GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function σT (√_s). In case of modeling the p-p interactions at the Large Hadron Collider (LHC), GEP is used to simulate and predict the total cross-section which is a function of total center-ofmass from low to high energy √_s. The discovered function shows a good match as compared with the other models. The predicted values of total cross section are in good agreement with Particle Data Group (PDG).