JAMP  Vol.4 No.1 , January 2016
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  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  as a function of the lab momentum , 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.
Cite this paper: El-Bakry, M. , El-Dahshan, E. , Radi, A. , Tantawy, M. , Moussa, M. (2016) Modeling and Simulation for High Energy Sub-Nuclear Interactions Using Evolutionary Computation Technique. Journal of Applied Mathematics and Physics, 4, 53-65. doi: 10.4236/jamp.2016.41009.

[1]   Jones, M.T. (2008) Artificial Intelligence: A Systems Approach Infinity. Science Press LLC, Hingham.

[2]   Banzhaf, W., et al. (1998) Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann, Burlington.

[3]   Higuchi, T., Liu, Y. and Yao, X. (2006) Evolvable Hardware. Genetic and Evolutionary Computation.

[4]   EvoNews Professor Hans-Paul Schwefel Talks to EvoNews (1999) Available Online.

[5]   Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966) Artificial Intelligence through Simulated Evolution. Wiley, New York.

[6]   Levenick, J.R (1991) Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology. Proceedings on the 4th International Conference on Genetic Algorithms.

[7]   Rechenberg I (1965) Cybernetic Solution Path of an Experimental Problem Technical Report Library Translation No. 1122. Royal Aircraft Establishment, Farnborough.

[8]   Zheng, S.J., Zhang, N., Xia, Y.J. and Wang, H.T. (2014) Research on Non-Uniform Strain Profile Reconstruction along Fiber Bragg Grating via Genetic Programming Algorithm and Interrelated Experimental Verification. Optics Communications, 315, 338-346.

[9]   Koza, J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge.

[10]   Koza, J.R. (1990) Genetic Programming: A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Stanford University Computer Science Department Technical Report STAN-CS-90-1314.

[11]   Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.

[12]   Tantawy, M., El-Mashad, M. and El-Bakry, M.Y. (1998) Multiparticle Production Process in High Energy Nucleus-Nucleus Collisions. Indian Journal of Physics, 72A, 73-82.

[13]   Moussa, M.A., El-Bakry, M.Y., Radi, A., El-Dahshan, E.-S.A., Habashy, D.M. and Abbas, E.G. (2012) Topological Cross Sections and Multiplicity Distributions for and Interactions at High Energies. International Journal of Scientific and Engineering Research, 3.

[14]   Fermi, E. (1950) High Energy Nuclear Events. Progress of Theoretical Physics, 5, 570-583.

[15]   Fermi, E. (1951) Angular Distribution of the Pions Produced in High Energy Nuclear Collisions. Physical Review, 81, 683-687.

[16]   Ranft, J. (1970) Secondary Particle Production According to the Thermodynamical Model and New Experimental Data. Physics Letters B, 31, 529-532.

[17]   Xu, C., Chao, W.-Q., Meng, T.-C. and Huang, C.-S. (1986) Statistical Approach to Nondiffractive Hadron-Hadron Collisions: Multiplicity Distributions and Correlations in Different Rapidity Intervals. Physical Review D, 33, 1287-1299.

[18]   Nambu, Y. (1976) The Confinement of Quarks. Scientific American, 235, 48-61.

[19]   Gyulassy, M. (1985) Introduction to QCD Thermodynamics and the Quark-Gluon Plasma. Progress in Particle and Nuclear Physics, 15, 403-442.

[20]   Kisslinger, L.S. (1985) Nuclear Physics and Quark/Gluon QCD. Nuclear Physics A, 446, 479-488.

[21]   Jacob, M. and Slansky, R. (1972) Nova Model of Inclusive Reactions. Physical Review D, 5, 1847-1870.

[22]   Hwa, R.C. (1970) Bootstrap Model for Diffractive Processes: Complementarity of the Yang and Regge Models. Physical Review D, 1, 1790-1809.

[23]   Hwa, R.C. (1971) Multiplicity Distribution and Single-Particle Spectrum in the Diffractive Model. Physical Review Letters, 26, 1143-1147.

[24]   EL-Bakry, S.Y., El-Dahshan, E.-S. and EL-Bakry, M.Y. (2011) Total Cross Section Prediction of the Collisions of Positrons and Electrons with Alkali Atoms Using Gradient Tree Boosting. Indian Journal of Physics, 85, 1405-1415.

[25]   El-Bakry, M.Y. (2003) Feed Forward Neural Networks Modeling for K-P Interactions. Chaos, Solitons and Fractals, 18, 995-1000.

[26]   El-Bakry, M.Y. (2004) A Study of K-P Interaction at High Energy Using Adaptive Fuzzy Inference System Interactions. International Journal of Modern Physics C, 15, 1013-1020.

[27]   El-Bakry, M.Y., El-Dahshan, E., Radi, A., Tantawy, M. and Moussa, M.A. (2013) A Genetic Programming for Modeling Hadron-Nucleus Interactions at 200 GeV/c. International Journal of Scientific and Engineering Research, 4, 7.

[28]   Ghosh, D. (1983) International Conference on Cosmic Ray 08.

[29]   De Marzo, C., De Palma, M., Distante, A., et al. (1982) Multiparticle Production on Hydrogen, Argon, and Xenon Targets in a Streamer Chamber by 200-GeV/c Proton and Antiproton Beams. Physical Review D, 26, 1019-1035.

[30]   Arneodo, M., Arvidson, A., Aubert, J.J., et al. (1987) Comparison of Multiplicity Distributions to the Negative Binomial Distribution in Muon-Proton Scattering. Zeitschrift für Physik C Particles and Fields, 35, 335-345.

[31]   Kittle, W. (1973) Combining Inclusive and Exclusive Data Analyses—What Have We Learned So Far? Journal of Physics A: Mathematical, Nuclear and General, 6, 733.

[32]   Abelev, B., et al., ALICE Collaboration (2013) Pseudorapidity Density of Charged Particles in p + Pb Collisions at s N N=5.02 TeV. Physical Review Letters, 110, Article ID: 032301.

[33]   Hong, W.-C. (2008) Rainfall Forecasting by Technological Machine Learning Models. Applied Mathematics and Computation, 200, 41-57.