Disentangling flow and nonflow correlations via Bayesian unfolding of the event-by-event distributions of harmonic coefficients in ultrarelativistic heavy-ion collisions
Jiangyong Jia, Soumya Mohapatra

TL;DR
This paper evaluates a Bayesian unfolding method for extracting event-by-event harmonic flow distributions in heavy-ion collisions, demonstrating its effectiveness in suppressing nonflow effects and analyzing flow fluctuations.
Contribution
It introduces and tests a Bayesian unfolding approach to accurately recover harmonic flow distributions, accounting for nonflow effects in heavy-ion collision data.
Findings
Unfolding recovers input v_2-v_4 distributions in simulations.
Nonflow effects are largely suppressed by the unfolding method.
Residual nonflow impacts are minimal on v_3 but affect v_2 and v_4 tails.
Abstract
The performance of the Bayesian unfolding method in extracting the event-by-event (EbyE) distributions of harmonic flow coefficients v_n is investigated using a toy model simulation, as well as simulations based on the HIJING and AMPT models. The unfolding method is shown to recover the input v_2-v_4 distributions for multiplicities similar to those observed in Pb+Pb collisions at the LHC. The effects of the nonflow are evaluated using HIJING simulation with and without a flow afterburner. The probability distribution of v_n resulting only from nonflow in HIJING is nearly a Gaussian and can be largely suppressed in the data-driven unfolding method used by the ATLAS Collaboration. The residual nonflow effects have no appreciable impact on the v_3 distributions, but are found to affect the tails of the v_2 and v_4 distributions; these effects manifest as a small simultaneous change in the…
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