TL;DR
This paper introduces a new method for analyzing long-range correlations in multiparticle production by examining joint factorial moments across multiple bins, significantly enhancing data discrimination.
Contribution
It proposes studying joint factorial moments or cumulants in several bins, which greatly improves the ability to distinguish different correlation patterns.
Findings
Enhanced discriminative power in data analysis.
Effective for studying long-range correlations.
Applicable to multiparticle production data.
Abstract
A new method to study the long-range correlations in multiparticle production is developped. It is proposed to study the joint factorial moments or cumulants of multiplicity distributions in several (more than two) bins. It is shown that this step dramatically increases the discriminative power of data.
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