Bayesian quantification of strongly interacting matter with color glass condensate initial conditions

The authors perform rigorous Bayesian inference on a variety of measurements in relativistic Pb-Pb collisions at the LHC using a comprehensive multistage model combining QCD-based initial states with viscous hydrodynamics and a hadronic afterburner. In particular, they extracted systematic constraints on the temperature dependence of shear and bulk viscosities of quark-gluon plasma that are significantly more precise due to improved physical models and statistical methods. For the range of plasma temperature probed in heavy-ion collisions, they find that the specific bulk viscosity demanded by the data is strongly non-zero and temperature-dependent, whereas the specific shear viscosity shows a much weaker temperature dependence that is indistinguishable from a constant value even with improved statistical analysis. Importantly, the authors showcase the application of transfer learning to efficiently explore a range of model uncertainties wider than had been considered previously. This work represents a substantial advancement in constraining the shear and bulk viscosities of strongly interacting matter.