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

A. Radi*

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةConference articleمراجعة النظراء

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 الأصليةEnglish
رقم المقال012049
دوريةJournal of Physics: Conference Series
مستوى الصوت869
رقم الإصدار1
المعرِّفات الرقمية للأشياء
حالة النشرPublished - يوليو 11 2017
الحدثInternational Conference Frontiers in Theoretical and Applied Physics, FTAPS 2017 - Sharjah, United Arab Emirates
المدة: فبراير ٢٢ ٢٠١٧فبراير ٢٥ ٢٠١٧

ASJC Scopus subject areas

  • ???subjectarea.asjc.3100.3100???

بصمة

أدرس بدقة موضوعات البحث “Studying total proton-proton cross section collision at large hadron collider using gene expression programming'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا