Applying Artificial Neural Network Proton - Proton Collisions at LHC

this paper shows that the use the optimal topology of an Artificial Neural Network (ANN) for a particular application is one of the difficult tasks. Neural Network is optimized by a Genetic Algorithm (GA), in a hybrid technique, to calculate the multiplicity distribution of the charged shower particles on Larger Hadron Collider (LHC). Moreover, ANN, as a machine learning technique, is usually used for modeling physical phenomena by establishing its new function. In case of modeling the p-p interactions at LHC experiments, ANN is used to simulate and predict the distribution, Pn, as a function of the number of charged particles multiplicity, n, the total center of mass energy ( s ), and the pseudo rapidity (). The discovered function, trained on experimental data of LHC, shows good match compared with the other models.