Probing charm quark dynamics via multiparticle correlations in PbPb collisions at $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV

TL;DR

This study measures multiparticle azimuthal correlations of prompt D0 mesons in PbPb collisions at 5.02 TeV, using a novel four-particle cumulant method to explore charm quark flow fluctuations and compare with theoretical models.

Contribution

It introduces the first use of four-particle cumulants to analyze D0 meson azimuthal anisotropy in heavy-ion collisions, providing new insights into charm quark flow fluctuations.

Findings

Four-particle $v_2$ ratios are comparable to charged particles but show deviations in extreme centralities.

Models with collisional energy loss better match the data than radiative models.

Charm quark flow fluctuations are observed and characterized across different collision centralities.

Abstract

Multiparticle azimuthal correlations of prompt D$^0$ mesons are measured in PbPb collisions at a nucleon-nucleon center-of-mass energy of $\sqrt{s_\mathrm{NN}} =$ 5.02 TeV. For the first time, a four-particle cumulant method is used to extract the second Fourier coefficient of the azimuthal distribution ($v_2$) of D$^0$ mesons as a function of event centrality and the D$^0$ transverse momentum. The ratios of the four-particle $v_2$ values to previously measured two-particle cumulant results provide direct experimental access to event-by-event fluctuations of charm quark azimuthal anisotropies. These ratios are also found to be comparable to those of inclusive charged particles in the event. However, hints of deviations are seen in the most central and peripheral collisions. To investigate the origin of flow fluctuations in the charm sector, these measurements are compared with models implementing fluctuations of charm quark energy loss via collisional or radiative processes in the quark-gluon plasma. These models cannot quantitatively describe the data over the full transverse momentum and centrality ranges, although the calculations with collisional energy loss provide a better description of the data.