Oscillations in modified combinants (C j s) have been of interest to multiparticle production mechanisms since the 1990s [1]. Recently, there has been a discussion on how these oscillations can be reproduced by compounding a binomial distribution with a negative binomial distribution [2, 3]. In this work, we explore a stochastic branching model based on a simple interaction term λψφψ for partons and propose a hadronization scheme to arrive at the final multiplicity distribution.We study the effects that compounding our model with a binomial distribution has on C j s and explore its physical implications. We find that there is a significant difference in the oscillations in C j s between high energy pp and pp scattering that our model can reproduce.

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The method of horizontal scaled factorial moments as outlined by Bialas and Peschanski was used to conduct intermittency analysis for pp collisions at √ s = 0.9, 7 and 8 TeV from the CMS experiment. The data was obtained and processed from the CERN Open Data Portal. It was found from 1D analysis that the intermittency strength decreases with increasing energy, indicating that the signature of the α-model of random cascading that the former is based on seems to be weakening. Intermittency was stronger in 2D, but did not reveal any clear trend with increasing collision energy.

Multifractal analysis was performed on pp collision data at √ s = 0.9, 7 and 8 TeV from the CMS experiment at CERN. The data was obtained and processed from the CERN Open Data Portal. Vertical analysis was used to compute the generalised dimensions Dq and the multifractal spectra f (α) of the data, which reveals the level of complexity of its pseudorapidity distribution. It was found that the f (α) curves widen with increasing collision energy, signalling an increase in branching complexity. Multifractal formalism for multiparticle productionMultifractal analysis is a powerful tool used to characterise the complexity of data. It is highly multidisciplinary in nature, finding applications in the analysis of a wide variety of complex systems, such as studying tectonic processes [1], medical signal analysis [2] and time series analysis in meteorology [3]. Here, we re-introduce a formalism tailored for investigating multiparticle production, as formulated by Hwa [4]. As with the original formulations, rapidity y will be used in the presentation, but will be substituted with pseudorapidity η when processing the data.