" Genetic Programming Modeling for Hadron-Hadron Interactions. "

Abstract

The present work deals with a theoretical study of high energy interactions of proton-proton interactions at high energies in the framework of Artificial Intelligence (AI) which contains the model of Genetic Programming (GP) for simulation these interactions and also prediction the collision of experiments that have not been made yet with describing them by mathematical function obtained from this model. The first chapter survived the experimental data of collisions at high energies and theoretical models which deal with this interactions as follows: 1. Pseudo-rapidity distributions for collisions at high and ultrahigh energies from UA5 ,E735 and LHC. 2. The Total, elastic and inelastic cross section for collisions at high and ultrahigh energies. 3. Multiplicity distributions for collisions at different energies experimentally from UA5 and LHC. 4. The different theoretical models which can be applied on these interactions with a brief explanation of the bases of these models. In the second chapter, we discussed the study of one model from Artificial Intelligence (AI) which is the model of the Genetic Programming (GP) for studying some characteristics of interactions. In the third chapter, Genetic Programming was designed for studying the pseudo-rapidity for interactions at = 23.6, 53, 200,546, 900 and 1800 GeV from UA5 and at LHC = 2.36 and 7 TeV.The GP is used to predict the experimental data at = 7 TeV and expect the energy 10 and 14 TeV that didn't be calculated experimentally. The simulation results performed almost exact fitting to the given experimental data and we compared with another models such as PYTHIA and PHOJET at = 10 and 14 TeV . In the forth chapter we study and calculate the total, elastic and inelastic cross sections for interaction using GP at = 7 TeV and 14 TeV. The results of the obtained mathematical function was good with experimental data and also predict total cross section at = 7 TeV and 14 TeV and it compared with another models. The GP shows a better fitting with experimental data.

3- GP also simulated and calculated the multiplicity distribution of at = 200, 546 ,900, and 1000 GeV from UA5 and E735 and = 2.36 and 7 TeV at LHC. The trained GP shows a better fitting with experimental data.The GP is used to predict the distributions at = 7 ,10and 14 TeV .The GP shows a good fitting with experimental data. This study refers to that the Genetic Programming (GP) can find the basic pattern information implied in a great number of experimental data,extract useful rules then apply these rules to obtain reasonable predicted results. So GP have become one of the important research areas in high energy physics.