Author(s)

El-Sayed A. El-Dahshan^1,2

¹Department of Physics, Faculty of Sciences, Ain Shams University, Cairo, Egypt.
²Egyptian E-Learning University (EELU), Giza, Egypt.

The multiplicity distribution (P(n_ch)) 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 <n_ch> 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(n_ch), as well as <n_ch> 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(n_ch) and <n_ch> which also, show a good agreement with different theoretical models.

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

El-Dahshan, E. (2017) PP and PP Multi-Particles Production Investigation Based on CCNN Black-Box Approach. Journal of Applied Mathematics and Physics, 5, 1398-1409. doi: 10.4236/jamp.2017.56115.