Studying Proton-Proton Interaction at Large Hadrons Collider Using Genetic Programming

Amr Radi*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


This paper describes how to use Genetic Programming (GP) as an evolutionary computational that is a family of algorithms for global optimization. GP, as a global optimization technique used by discovery of a new function for modeling physical phenomena. The p-p interactions are modeled at Large Hadron Collider (LHC) experiments, the number of charged particles multiplicity >n< and the total cross-section, σT, as functions of the total center of mass energy (from low to ultra-high energy), √s are discovered by using GP. In view of the discovered function for >n< (√s), the overall trend of the values predicted is consistent with LHC data [predicted values are 34.8638 and 35.3520 at √s = 13 TeV and √s = 14 TeV respectively]. The new function σT (√s) trained on experimental data of Particle Data Group (PDG) demonstrates a nice match to the other models. The predicted values of the total cross section at √ s = 13 TeV, and 14 TeV are found to be 109.0381 mb and 111.8329 mb respectively. Furthermore, the values predicted are agreed with other models like Block.

Original languageEnglish
Article number012013
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - Oct 21 2019
Event1st Sharjah International Conference on Particle Physics, Astrophysics and Cosmology, FISICPAC 2018 - Sharjah, United Arab Emirates
Duration: Nov 11 2018Nov 13 2018

ASJC Scopus subject areas

  • General Physics and Astronomy


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