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

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Abstract

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).

Original languageEnglish
Article number012049
JournalJournal of Physics: Conference Series
Volume869
Issue number1
DOIs
Publication statusPublished - Jul 11 2017

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gene expression
programming
collisions
protons
cross sections
machine learning
energy
interactions

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

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abstract = "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).",
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