Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium

摘要

We quantitatively estimate properties of the quark-gluon plasma created inultra-relativistic heavy-ion collisions utilizing Bayesian statistics and amulti-parameter model-to-data comparison. The study is performed using arecently developed parametric initial condition model, TRENTO, whichinterpolates among a general class of particle production schemes, and a modernhybrid model which couples viscous hydrodynamics to a hadronic cascade. Wecalibrate the model to multiplicity, transverse momentum, and flow data andreport constraints on the parametrized initial conditions and thetemperature-dependent transport coefficients of the quark-gluon plasma. We showthat initial entropy deposition is consistent with a saturation-based picture,extract a relation between the minimum value and slope of thetemperature-dependent specific shear viscosity, and find a clear signal for anonzero bulk viscosity.