Unfolding of event-by-event net-charge distributions in heavy-ion collision

TL;DR

This paper presents a Bayesian unfolding method to accurately recover true event-by-event net-charge distributions in heavy-ion collisions from measured data affected by detector inefficiencies, enabling more reliable physical interpretations.

Contribution

The paper introduces a Bayesian unfolding technique that effectively corrects for detector effects in net-charge distributions, preserving higher moments crucial for physics analysis.

Findings

Successfully unfolded distributions match true distributions across various conditions.

Higher moments like skewness and kurtosis are reliably recovered after unfolding.

The method eliminates the need for new observables to cancel detector effects.

Abstract

We discuss a method to obtain the true event-by-event net-charge multiplicity distributions from a corresponding measured distribution which is subjected to detector effects such as finite particle counting efficiency. The approach is based on the Bayes method for unfolding of distributions. We are able to faithfully unfold back the measured distributions to match with their corresponding true distributions obtained for a widely varying underlying particle production mechanism, beam energy and collision centrality. Particularly the mean, variance, skewness, kurtosis, their products and ratios of net-charge distributions from the event generators are shown to be successfully unfolded from the measured distributions constructed to mimic a real experimental distribution. We demonstrate the necessity to account for detector effects before associating the higher moments of net-charge distributions with physical quantities or phenomena. The advantage of this approach being that one need not construct new observable to cancel out detector effects which loose their ability to be connected to physical quantities calculable in standard theories.