Bayesian estimation of the specific shear and bulk viscosity of the quark-gluon plasma with additional flow harmonic observables
J.E. Parkkila, A. Onnerstad, D.J. Kim

TL;DR
This study uses Bayesian methods with advanced flow harmonic observables to estimate the transport properties of quark-gluon plasma, revealing weaker temperature dependence of shear viscosity and lower bulk viscosity than prior studies.
Contribution
First to incorporate sophisticated flow harmonic observables into Bayesian analysis for quark-gluon plasma transport properties, improving constraints on viscosity parameters.
Findings
Weaker temperature dependence of shear viscosity compared to previous studies.
Lower preferred bulk viscosity and higher switching temperature.
Symmetric cumulants and non-linear flow modes are most sensitive for constraining viscosities.
Abstract
The transport properties of the strongly-coupled quark-gluon plasma created in ultra-relativistic heavy-ion collisions are extracted by Bayesian parameter estimate methods with the latest collision beam energy data from LHC. This Bayesian analysis includes sophisticated flow harmonic observables for the first time. We found that the temperature dependence of specific shear viscosity shows weaker than the previous studies. The results prefer a lower value of specific bulk viscosity and a higher switching temperature to reproduce additional observables. However, the improved statistical uncertainties both on the experimental data and hydrodynamic calculations with additional observables do not help to reduce the final credibility ranges much, indicating a need for improving the dynamical collision model before the hydrodynamic takes place. In addition, the sensitivities of experimental…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
