Analyses of multiplicity distributions and Bose-Einstein correlations at the LHC using negative binomial distribution and generalized Glauber-Lachs formula

TL;DR

This paper compares the effectiveness of the double-generalized Glauber-Lachs formula and the double-negative binomial distribution in analyzing multiplicity distributions and Bose-Einstein correlations at the LHC, revealing their comparable performance and parameter relationships.

Contribution

It introduces the application of D-GGL and D-NBD models to LHC data, demonstrating their effectiveness and parameter correlations in multiplicity and correlation analyses.

Findings

D-GGL performs as effectively as D-NBD in data analysis.

Parameters in multiplicity distributions relate to those in Bose-Einstein correlations.

Both models successfully describe LHC multiplicity and correlation data.

Abstract

This study aims to analyze the data on multiplicity distributions and Bose-Einstein correlations collected at the LHC by the ATLAS and CMS Collaborations using a double-generalized Glauber-Lachs formula (D-GGL) and double-negative binomial distribution (D-NBD). From this investigation, it can be inferred that the D-GGL formula performs as effectively as the D-NBD. Moreover, our results show that the parameters estimated in multiplicity distributions (MD) (P(n)) are related to those contained in the BEC formula.