Application of gene expression programming for proton-proton interactions at large hadrons collider

A. Radi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper describes how to use gene expression programming (GEP) as an evolutionary computational optimization approach. GEP, as a machine learning technique is usually used for modeling physical phenomena by discovering a new function. In case of modeling the p-p interactions at large hadrons collider (LHC) experiments, GEP is used to simulate and predict the number of charged particles multiplicity 〈 n 〉 and total cross-section, σ T, as a function of total center-of-mass energy, s. Considering the discovered function for 〈 n 〉 (s), the general trend of the predicted values shows good agreement at LHC [predicted values are 31.3251, 32.8638 and 35.3520 at s = 8 TeV, s = 10 TeV and s = 14 TeV respectively]. The discovered function, trained on experimental data of particle data group shows a good match as compared with the other models. The predicted values of cross section at s = 8, 10 and 14 TeV are found to be 101.0417, 105.0690 and 111.3407 mb respectively. Moreover, those predicted values are in good agreement with those reported by Block, Cudell and Nakamura.

Original languageEnglish
Pages (from-to)593-599
Number of pages7
JournalIndian Journal of Physics
Issue number6
Publication statusPublished - Jun 2013
Externally publishedYes


  • Gene expression programming
  • Machine learning
  • Modeling
  • Multiplicity distribution
  • Proton-proton interaction

ASJC Scopus subject areas

  • General Physics and Astronomy


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