Modeling charged-particle multiplicity distributions at LHC

Amr Radi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


With many applications in high-energy physics, Deep Learning or Deep Neural Network (DNN) has become noticeable and practical in recent years. In this article, a new technique is presented for modeling the charged particles multiplicity distribution Pn of Proton-Proton (PP) collisions using an efficient DNN model. The charged particles multiplicity n, the total center of mass energy s, and the pseudorapidity η used as input in DNN model and the desired output is Pn. DNN was trained to build a function, which studies the relationship between Pn n,s,η. The DNN model showed a high degree of consistency in matching the data distributions. The DNN model is used to predict with Pn not included in the training set. The expected Pn had effectively merged the experimental data and the values expected indicate a strong agreement with Large Hadron Collider (LHC) for ATLAS measurement at s = 0.9, 7 and 8 TeV.

Original languageEnglish
Article number2050302
JournalModern Physics Letters A
Issue number36
Publication statusPublished - Nov 30 2020


  • Charged particles multiplicity distribution
  • charged particles multiplicity
  • deep neural network
  • the total center of mass energy, and the pseudorapidity

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Astronomy and Astrophysics
  • Physics and Astronomy(all)


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