ملخص
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).
اللغة الأصلية | English |
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رقم المقال | 012049 |
دورية | Journal of Physics: Conference Series |
مستوى الصوت | 869 |
رقم الإصدار | 1 |
المعرِّفات الرقمية للأشياء | |
حالة النشر | Published - يوليو 11 2017 |
الحدث | International Conference Frontiers in Theoretical and Applied Physics, FTAPS 2017 - Sharjah, United Arab Emirates المدة: فبراير ٢٢ ٢٠١٧ → فبراير ٢٥ ٢٠١٧ |
ASJC Scopus subject areas
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