Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique

Abstract

High energy sub-nuclear interactions are a good tool to dive deeply in the core of the particles to recognize their structures and the forces governed. The current article focuses on using one of the evolutionary computation techniques, the so-called genetic programming (GP), to model the hadron nucleus (h-A) interactions through discovering functions. In this article, GP is used to simulate the rapidity distribution       N N Y 1 d d of total charged, positive and negative pions for p −-Ar and p −-Xe interactions at 200 GeV/c and charged particles for p-pb collision at 5.02 TeV. We have done so many runs to select the best runs of the GP program and finally obtained the rapidity distribution       N N Y 1 d d as a function of the lab momentum () Lab P , mass number (A) and the number of particles per unit solid angle (Y). In all cases studied, we compared our seven discovered functions produced by GP technique with the corresponding experimental data and the excellent matching was so clear.

The Application of the Genetic Algorithm-Back Propagation Neural Network Algorithm in the High-Energy Physics

“…Rapidity distribution of negative, positive and charged pions produced from p -Au, p -Ag and p -Mg collisions at GA-BPNN model has been shown to be a useful method for modeling the h-A interactions. All the figures showed a clear and excellent match to the experimental data [17][18][19][20][21].…”

“…To illustrate the performance of genetic algorithm for designing and optimizing the model parameters (weights and the threshold) of neural networks, this work used the same sample data. That is, we randomly selected 4 groups of data as the training sample and the rest was selected as test data to observe the training and testing performance of rapidity distribution for (h-A) collisions at Using this input -output arrangement, the optimal network with four different connection parameter configurations (weights and biases) were tried to achieve good mean sum squared error (MSE) and good performance for the positive and negative pions for p Au, p Ag and p Mg − − − interactions at Lab P =100 GeV [17] the rapidity distribution of created (total charged, positive and negative) pions for p Ar,p Xe The transfer functions of the hidden layers were chosen to be a tan sigmoid for the all networks, while the output layer was chosen to be a pure line. The trained algorithm which used to train the ANN model (for the four interactions) is LM optimization technique, with number of epochs=2000.…”

“…of positive and negative pions for p-Au, p-Ag and p-Mg interactions at 100 GeV/c [17], the rapidity distribution of created (total charged, positive and negative) pions for p Ar, p Xe − − collisions at Lab P =200 GeV/c [18][19][20] and the rapidity distribution of total charged particles for p Pb − collision at ( ) s = 5.02 TeV [21]. The discovered functions produced by the hybrid model show an excellent matching when they have been compared to the corresponding experimental data [18][19][20][21].…”