Applying artificial neural network and deep learning in PP and PP ^- collisions cross section

In recent years, Deep Learning (DL) has become visible and operable framework with many applications in High Energy Physics. In this paper we use DL to calculate, the total cross section (σ_tot) of both proton-proton (PP) and proton-antiproton (PP) collisions from low to ultra-high energy regions as function of center of mass energy (√s) and the Type of Particle Collision (TPC). √s and TPC are used as inputs in DL and σ_tot is the desired output. DL has been trained to construct a function that studies the relation between σ_tot ((√s), TPC). The trained DL model has shown a high degree of performance in matching the trained distributions. The DL is used to forecast with σ_tot that is not given in the training set. The predicted σ_tot had been combined the experimental data effectively.