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 offequilibrium 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 eventbyevent heavyion collision model at a small set of points in the multidimensional parameter space, varying all parameters simultaneously. Gaussian process emulators are used to nonparametrically 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 temperaturedependent specific shearviscosity of the QuarkGluonPlasma with uncertainty quantification.
Seminar
Linderman Library 317
