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Beggio, P.C. and Luna, E.G.S. (2014) Cross Sections, Multiplicity and Moment Distributions at the LHC. Nuclear Physics A, 929, 230-245.

has been cited by the following article:

  • TITLE: PP and PP Multi-Particles Production Investigation Based on CCNN Black-Box Approach

    AUTHORS: El-Sayed A. El-Dahshan

    KEYWORDS: Proton-Proton and Proton-Antiproton Collisions, Multiparticle Production, Multiplicity Distributions, Intelligent Computational Techniques, CCNN-Neural Networks, Black-Box Modeling Approach

    JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.5 No.6, June 30, 2017

    ABSTRACT: The multiplicity distribution (P(nch)) of charged particles produced in a high energy collision is a key quantity to understand the mechanism of multiparticle production. This paper describes the novel application of an artificial neural network (ANN) black-box modeling approach based on the cascade correlation (CC) algorithm formulated to calculate and predict multiplicity distribution of proton-proton (antiproton) (PP and PP ) inelastic interactions full phase space at a wide range of center-mass of energy . In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution nch> as a function of . The CCNN model was designed based on available experimental data for = 30.4 GeV, 44.5 GeV, 52.6 GeV, 62.2 GeV, 200 GeV, 300 GeV, 540 GeV, 900 GeV, 1000 GeV, 1800 GeV, and 7 TeV. Our obtained empirical results for P(nch), as well as nch> for (PP and PP) collisions are compared with the corresponding theoretical ones which obtained from other models. This comparison shows a good agreement with the available experimental data (up to 7 TeV) and other theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV) we have predicted P(nch) and nch> which also, show a good agreement with different theoretical models.