Speakers
Description
Event-by-event fluctuations in the number of different particle species produced in high-energy nuclear collisions encode essential information about the phase structure of the matter created in such collisions. In this contribution, we present a novel fuzzy-logic-based approach for reconstructing arbitrary-order moments of multiplicity distributions [1]. The proposed method provides a robust alternative to traditional cut-based (binary) particle identification techniques, enabling a more precise reconstruction of higher-order moments in particle multiplicity distributions. By leveraging fuzzy logic, this approach addresses the uncertainties inherent in overlapping detector signals, thereby enhancing the reliability of the measurements. The mathematical framework developed here is shown to be effective across various simulation models, demonstrating its versatility. Furthermore, we test the method’s robustness by introducing correlations in particle production using the Metropolis algorithm, which verifies its applicability even in complex, correlated scenarios. This framework opens new avenues for conducting more accurate event-by-event analyses in high-energy nuclear physics experiments, offering insights into the phase structure of the matter produced.
[1] A. Rustamov, Fuzzy logic for reconstructing arbitrary moments of multiplicity distributions, e-Print: 2409.09814 [nucl-th]
| Category | Experiment |
|---|
