Bulg. J. Phys. vol.45 no.3 (2018), pp. 299-312

Investigation of Inelastic Cross Sections for pp and pp Collisions Using RBF Intelligent Computing Approach

El-Sayed A. El-Dahshan1,2
1Department of Physics, Faculty of Sciences, Ain Shams University, Abbassia, 11566, Cairo, Egypt
2Egyptian E-Learning University (EELU), 33 Elmesah Street, Eldoki, l1261, El-Geiza, Egypt
Abstract. The inelastic cross-section is an important observable in high and ultra-high-energy cosmic rays and hadronic interactions. In the present work, radial basis functions (RBF)-based intelligent computing (IC) model is presented for modeling the inelastic cross-sections of both pp and pp collisions (σinels) from low to ultra-high energy (from below the ISR to the most recent LHC). Radial basis functions (RBF) neural networks are intelligent and powerful algorithms that can be used for function approximation and nonlinear modeling. The RBF-based IC model has been developed and trained, based on the available experiment to model (and approximate) the inelastic cross-section data of both pp and pp collisions as a function of center-of-mass-energy (√s). Our obtained results of σinels for pp and pp are compared with other theoretical calculations and predictions. It is found that the calculated and predicted cross-sections highly agree with both the available experimental measurements and theoretical results as well as the predictions of the other models. The results of our developed model show a good performance in modeling the experimental data and prediction for the unseen values of σinels.

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