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

Frontiers in Theoretical and Applied Physics/UAE 2017

Radi, A.^1,2

Department of Physics, College of Sciences, Sultan Qaboos University, Al Khoudh, Muscat

123, Oman^1

Department of Physics, Faculty of Sciences, Ain Shams University, Abbassia, Cairo

11566, Egypt^2

关键词: Gene expression programming;    Large Hadron Collider;    Large Hadron collider LHC;    Machine learning techniques;    New functions;    Particle data groups;    Physical phenomena;    Ultra-high energies;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/869/1/012049/pdf
DOI  :  10.1088/1742-6596/869/1/012049

来源: IOP

【 摘 要 】

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).