Proton--proton and proton--antiproton differential elastic cross sections modeling at high and ultra-high energies using a hybrid computing paradigm

Authors : Elsayed ELDAHSHAN

Pages : 426-435

View : 27 | Download : 6

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This work presents a hybrid computing technique for modeling the differential elastic cross section of both proton--proton ``pp`` and proton--antiproton ``ppinsert ignore into journalissuearticles values(bar);`` collisions from high to ultra-high energy regions insert ignore into journalissuearticles values(from 13.9 GeV to 14 TeV); as a function of the center-of-mass energy ``s`` squared and four momentum transfer squared ``t``. We proposed a genetic algorithm insert ignore into journalissuearticles values(GA); and support vector regression insert ignore into journalissuearticles values(SVR); hybrid techniques to calculate and predict the ``differential elastic cross section`` of both ``pp`` and ``ppinsert ignore into journalissuearticles values(bar);``. Our proposed GA-SVR hybrid model shows a good match to the available experimental data, as well as predicting the latest and future ``TOTEM`` experiments for differential elastic cross sections that are not utilized in the training phase. An extrapolation of the differential cross section to $\vert t\vert \to $0 gives the total cross section through the optical theorem. The model calculations and predictions for ``pp`` and ``ppinsert ignore into journalissuearticles values(bar);`` total cross-sections over a wide collision energy ranging from ISR to LHC are given. All the results for the total cross-sections matched well with the last and the more recent measurements insert ignore into journalissuearticles values($\sqrt{ s}$= 7 TeV and 8 TeV);, as well as the results of other models for the future ``TOTEM`` measurements at insert ignore into journalissuearticles values($\sqrt{ s}$ = 10 TeV and 14 TeV);.
Keywords : Deferential elastic cross sections, proton proton antiproton, collision, empirical modeling, support vector regression, genetic algorithm