Amr Radi
Sultan Qaboos University, Oman
Title: Studying total cross section for proton-proton interactions at large hadrons collider
Biography
Biography: Amr Radi
Abstract
This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p–p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the total cross-section, as a function of total center-of-mass from low to high energy √s, Considering the discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of total cross section at √s = 8, 10 and 14 TeV are found to be 10, 105 and 111 mb, respectively. Moreover, those predicted values are in good agreement with those reported by Nakamura, Cudell and Block.