Development of particle multiplicity distributions using a general form of the grand canonical partition function and applications to L3 and H1 Data

Details Development of particle multiplicity distributions using a general form of the grand canonical partition function and applications to L3 and H1 Data Development of particle multiplicity distributions using a general form of the grand canonical partition function and applications to L3 and H1 Data

2003-04-17

arxiv.org/abs/nucl-th/0304057v1

Nucl.Phys. A730 (2004) 514-547

Physics

Nuclear Physics

Nuclear Theory

7 figures included

Scientific paper

Various phenomenological models of particle multiplicity distributions are discussed using a general form of a unified model which is based on the grand canonical partition function and Feynman's path integral approach to statistical processes. These models can be written as special cases of a more general distribution which has three control parameters which are $a$, $x$, $z$. The relation to these parameters to various physical quantities are discussed. A connection of the parameter $a$ with Fisher's critical exponent $\tau$ is developed. Using this grand canonical approach, moments, cumulants and combinants are discussed and a physical interpretation of the combinants are given and their behavior connected to the critical exponent $\tau$. Various physical phenomena such as hierarchical structure, void scaling relations, KNO scaling features, clan variables, and branching laws are shown in terms of this general approach. Several of these features which were previously developed in terms of the negative binomial distribution are found to be more general. Both hierarchical structure and void scaling relations depend on the Fisher exponent $\tau$. Applications of our approach to the charged particle multiplicity distribution in jets of L3 and H1 data are given. It is shown that just looking at the mean and fluctuation of data is not enough to distinguish these distributions or the underlying mechanism. The mean, fluctuation and third cummulant of distribution determine three parameters $x$, $z$, $a$. We find that a generalized random work model fits the data better than the widely used negative binomial model.

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