<i>PP</i> and <i>P<span style='text-decoration:overline;'>P</span></i> Multi-Particles Production Investigation Based on CCNN Black-Box Approach

Abstract

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) ( and PP PP ) inelastic interactions full phase space at a wide range of center-mass of energy s . 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 s . The CCNN model was designed based on available experimental data for s = 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 ( and PP 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 ( s = 14 TeV) we have predicted P(n ch ) and <n ch > which also, show a good agreement with different theoretical models.