Physics Colloquium: “A data-driven approach to quantifying the shear viscosity of nature’s most ideal liquid” with Dr. Steffen A. Bass

Over the last few years the Duke QCD group has developed techniques based on Bayesian statistics that allow for the simultaneous calibration of a large number of model parameters and the precision extraction of QGP properties including their quantified uncertainties. The computational model used is based on the Trento initial condition model, viscous relativistic hydrodynamics and a Boltzmann equation based transport model to describe the off-equilibrium late stage hadronic evolution. The analysis starts by selecting a set of salient model parameters -- including physical properties such as temperature and/or momentum dependent transport coefficients -- then evaluates the event-by-event heavy-ion collision model at a small set of points in the multidimensional parameter space, varying all parameters simultaneously. Gaussian process emulators are used to non-parametrically interpolate the parameter space, providing fast predictions at any point in parameter space with quantitative uncertainty. Finally, the parameter space is systematically explored using a Markov chain Monte Carlo (MCMC) to obtain rigorous constraints on all parameters simultaneously, including all correlations among the parameters. In this talk I will review the basic components of the Bayesian analysis that have led to the first determination of the temperature-dependent specific shear-viscosity of the Quark-Gluon-Plasma with uncertainty quantification.