How to retrieve additional information from the multiplicity distributions

TL;DR

This paper explores methods to extract more detailed information from multiplicity distributions in particle physics, demonstrating that combining models reveals oscillatory behaviors in high-energy collision data.

Contribution

It introduces a modified clan model with NBD parameters depending on multiplicity and combines it with cascade-stochastic formalism to better analyze multiplicity distributions.

Findings

Single NBD with N-dependent parameters can describe data

Combined approach reveals oscillatory behavior in distributions

Enhanced understanding of particle production mechanisms

Abstract

Multiplicity distributions $P(N)$ measured in multiparticle production processes are most frequently described by the Negative Binomial Distribution (NBD). However, with increasing collision energy some systematic discrepancies become more and more apparent. They are usually attributed to the possible multi-source structure of the production process and described using a multi-NBD form of the multiplicity distribution. We investigate the possibility of keeping a single NBD but with its parameters depending on the multiplicity $N$. This is done by modifying the widely known clan model of particle production leading to the NBD form of $P(N)$. This is then confronted with the approach based on the so-called cascade-stochastic formalism which is based on different types of recurrence relations defining $P(N)$. We demonstrate that a combination of both approaches allows the retrieval of additional valuable information from the multiplicity distributions, namely the oscillatory behavior of the counting statistics apparently visible in the high energy data.