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Advances in High Energy Physics
Volume 2018 (2018), Article ID 2136908, 11 pages
https://doi.org/10.1155/2018/2136908
Research Article

Entropy and Multifractality in Relativistic Ion-Ion Collisions

Department of Physics, Aligarh Muslim University, Aligarh 202002, India

Correspondence should be addressed to Shakeel Ahmad; hc.nrec@damha.leekahs

Received 31 August 2017; Revised 5 January 2018; Accepted 11 March 2018; Published 11 April 2018

Academic Editor: Raghunath Sahoo

Copyright © 2018 Shaista Khan and Shakeel Ahmad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The publication of this article was funded by SCOAP3.

Abstract

Entropy production in multiparticle systems is investigated by analyzing the experimental data on ion-ion collisions at AGS and SPS energies and comparing the findings with those reported earlier for hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is observed that the entropy produced in limited and full phase space, when normalized to maximum rapidity, exhibits a kind of scaling which is nicely supported by Monte Carlo model HIJING. Using Rényi’s order information entropy, multifractal characteristics of particle production are examined in terms of generalized dimensions, . Nearly the same values of multifractal specific heat, , observed in hadronic and ion-ion collisions over a wide range of incident energies suggest that the quantity might be used as a universal characteristic of multiparticle production in hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. The analysis is extended to the study of spectrum of scaling indices. The findings reveal that Rényi’s order information entropy could be another way to investigate the fluctuations in multiplicity distributions in terms of spectral function , which has been argued to be a convenient function for comparison sake not only among different experiments but also between the data and theoretical models.