Information-field-based global Bayesian inference of the jet transport coefficient

Abstract

All recent analyses of heavy-ion data that extract information about the transport properties of excited strongly interacting matter use Bayesian techniques to fit parameters of functional forms motivated by physical insight. These explicit parameterizations may introduce undesired long-range correlations between different regions of parameter space. This paper develops an information field approach to avoid an explicit functional parameterization and obtain a global Bayesian inference of the jet transport coefficient $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{q}$ as a function of the temperature.