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Clan-Model of Particle Production Process-Revisited in Chaos-based Complex Network Scenario

  • Physics of Elementary Particles and Atomic Nuclei. Experiment
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Abstract

The multiplicity-distribution of High-Energy-Interaction, had earlier been analysed w.r.t. clan-model and Negative-Binomial-Distribution (NBD), which described the underlying particle production process by means of cascading mechanism. Clan was defined to contain the particles stemming from the same ancestor [1] and Average-multiplicity within clan was deduced from the best fitted NBD for the multiplicity-distribution of full-phase-space. Since latest data couldn’t be confronted with NBD alone to deduce number of clans and their internal dynamics, we propose a new rigorous method using complex-network and chaos-based Visibility-Graph-algorithm to extract clusters from different rapidity-regions around the central-rapidity-(cr) of exemplary data of 32S-AgBr(200 A GeV)-interaction. These clusters are found to be scale-free and self-similar. For each cluster Power-of-Scale-freeness-of-Visibility-Graph (PSVG) and two important topological parameters: Average-clustering-co-efficient, Average-degree are extracted based on visibility of the nodes from each other in the Visibility Graph constructed from the cluster. The clan-model is revisited by correlating clusters with clans and Average-degree of the clusters with the number of particles in the clan. For each rapidity-region around (cr) the number of clusters/clans having higher values of both Average-clustering-co-efficient and PSVG, is extracted. For those clusters (Average-degree)/(number-of-particles-in-the-clan) has been calculated. It has been found that there are fewer number of clusters/clans having higher Average- clustering-co-efficient and PSVG, in the rapidity-region nearest to the (cr) and this count increases monotonically across increasing overlapping rapidity-regions around (cr)

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Bhaduri, S., Bhaduri, A. & Ghosh, D. Clan-Model of Particle Production Process-Revisited in Chaos-based Complex Network Scenario. Phys. Part. Nuclei Lett. 15, 446–455 (2018). https://doi.org/10.1134/S1547477118040040

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