%0 Journal Article
%T <i>PP</i> and <i>P<span style='text-decoration:overline;'>P</span></i> Multi-Particles Production Investigation Based on CCNN Black-Box Approach
%A El-Sayed A. El-Dahshan
%J Journal of Applied Mathematics and Physics
%P 1398-1409
%@ 2327-4379
%D 2017
%I Scientific Research Publishing
%R 10.4236/jamp.2017.56115
%X The multiplicity distribution (<i>P</i>(<i>n<sub>ch</sub></i>)) 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) (<i>PP </i>and <i>P<span style=\"text-decoration:overline;\">P</span></i> ) inelastic interactions full phase space at a wide range of center-mass of energy <img alt=\"\" src=\"Edit_97e3dfc5-efa8-4d54-96fd-e9e9cd7faaaa.bmp\" />. In addition, the formulated cascade correlation neural network (CCNN) model is used to empirically calculate the average multiplicity distribution &lt;<i>n<sub>ch</sub></i>&gt; as a function of <img alt=\"\" src=\"Edit_7dc84682-c99b-4968-9f4b-b8414f1a83f4.bmp\" />. The CCNN model was designed based on available experimental data for <img alt=\"\" src=\"Edit_02e7ccff-b3e4-48df-8513-ea890339d150.bmp\" />= 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(<i>n<sub>ch</sub></i>), as well as &lt;<i>n<sub>ch</sub></i>&gt; for (<i>PP </i>and <i>P<span style=\"text-decoration:overline;\">P</span></i>) 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 ( <img alt=\"\" src=\"Edit_3877cb74-952c-4d60-9695-f27d8ad156b0.bmp\" />= 14 TeV) we have predicted P(<i>n<sub>ch</sub></i>) and &lt;<i>n<sub>ch</sub></i>&gt; which also, show a good agreement with different theoretical models.
%K Proton-Proton and Proton-Antiproton Collisions
%K Multiparticle Production
%K Multiplicity Distributions
%K Intelligent Computational Techniques
%K CCNN-Neural Networks
%K Black-Box Modeling Approach
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=77344