Modeling p′p and recent LHC pp total cross-sections

Amr Radi, Esraa El-Khateeb

Research output: Contribution to journalArticle

Abstract

New technique is presented for modeling total cross-section of both pp and $\bar{p}p$ collisions from low to ultra high energy regions using an efficient artificial neural network (ANN). We have used the input (center-of-mass energy, √s, and type of particle P) and output (total cross-section σtot) data to build a prediction model by ANN. The neural network has been trained to produce a function that studies the dependence of σtot on √s and P. The trained ANN model shows a good performance in matching the trained distributions, predicts cross-sections that are not presented in the training set. The general trend of the predicted values shows a good agreement with the recent Large Hadron Collider (LHC) measurements, where the total cross-section at √s = 7∼{\rm TeV}$ and 8 TeV are measured to be 98.6 mb and 101.7 mb, respectively. The predicted values of the total cross-section at √s = 10∼{\rm TeV} and 14 TeV are found to be 105.8 mb and 111.7 mb, respectively. Those predictions are in good agreement with Block, Cudell and Nakamura.

Original languageEnglish
Article number1450044
JournalModern Physics Letters A
Volume29
Issue number8
DOIs
Publication statusPublished - Mar 14 2014

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Keywords

  • artificial neural networks.
  • Hadronic collisions
  • pp collisions
  • p′p collisions
  • total cross-section

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Astronomy and Astrophysics

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