Application of genetic programming for proton-proton interactions

Abstract

Abstract:The aim of the present work is to use one of the machine learning techniques named the genetic programming (GP) to model the p-p interactions through discovering functions. In our study, GP is used to simulate and predict the multiplicity distribution of charged pions (P( )), the average multiplicity ( ) and the total cross section (σ ) at different values of high energies. We have obtained the multiplicity distribution as a function of the center of mass energy ( √ ) and charged particles ( ). Also, both the average multiplicity and the total cross section are obtained as a function of √ . Our discovered functions produced by GP technique show a good match to the experimental data. The performance of the GP models was also tested at non-trained data and was found to be in good agreement with the experimental data.PACS (