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Research Article

Academia Journal of Scientific Research 8(12): 383-386, December 2020
DOI: 10.15413/ajsr.2020.0114
ISSN 2315-7712
2020 Academia Publishing

Abstract


Some evidences of multiplicity fluctuations in relativistic nuclear collisions and Neural Networking (N-N) model
 

 

Accepted 7th February, 2019

M. Ayaz Ahmad1, Anghel Drugarin C. Victoria2, Jalal H. Baker1, Vyacheslav V. Lyashenko3

1Physics Department, Faculty of science, University of Tabuk, Tabuk - 71451, Saudi Arabia, P.O. Box 741.
2Department of Electronics and Informatics Engineering “Eftimie Murgu”, University of Resita, Resita, Romania.
3Laboratory “Transfer of Information Technologies in the risk reduction systems”, Department of Informatics, Kharkov National University of Radio Electronics, Ukraine.

This study attempted to investigate some features of non-thermal phase transitions during the relativistic heavy ion collisions. The collisions that occurred were a beam of 28Si (projectile) hits the heterogeneous mixture of nuclear emulsion (fixed target) @ energy 14.6A GeV and we recorded 951 events of relativistic nuclear collisions. Thereafter, for the final statistical study, we made three different groups of data sets such as 28Si+CNO, 28Si+Emulsion and 28Si+AgBr. The results of the experiment was compared with neural network (N-N) model and a good agreement found between the theory and experiment of relativistic heavy ion collisions.

Key words: Intermittency, Scaled Factorial Moments (SFMs) and Non thermal Phase transition.
 

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article as:

Ahmad MA, Victoria ADC, Baker JH, Lyashenko VV (2020). Some evidences of multiplicity fluctuations in relativistic nuclear collisions and Neural Networking (N-N) model. Acad. J. Sci. Res. 8(12): 383-386.

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