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

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 model and a multiphase transport (AMPT) model. The unfolding method is shown to recover the input v₂-v₄ distributions for events with multiplicities similar to those observed in Pb+Pb collisions at the Large Hadron Collider. The effects of the nonflow are evaluated using the HIJING simulation with and without a flow afterburner. The probability distribution of v_n from nonflow is nearly a Gaussian and can be largely suppressed with the data-driven unfolding method used by the ATLAS Collaboration. The residual nonflow effects have no appreciable impact on the v₃ distributions, but affect the tails of the v₂ and v₄ distributions; these effects manifest as a small simultaneous change in the mean and standard deviation of the v_n distributions. For the AMPT model, which contains both flow fluctuations and nonflow effects, the reduced shape of the extracted v_n distributions is found to be independent of p _T in the low-p_T region, similar to what is observed in the ATLAS data. The prospect of using the EbyE distribution of the harmonic spectrum aided by the unfolding technique as a general tool to study azimuthal correlations in high-energy collisions is also discussed.