Bayesian Parameter Estimation for Relativistic Heavy-ion Collisions

Abstract

I develop and apply a Bayesian method for quantitatively estimating properties of the quark-gluon plasma (QGP), an extremely hot and dense state of fluid-like matter created in relativistic heavy-ion collisions.The QGP cannot be directly observed---it is extraordinarily tiny and ephemeral, about 10^(-14) meters in size and living 10^(-23) seconds before freezing into discrete particles---but it can be indirectly characterized by matching the output of a computational collision model to experimental observations.The model, which takes the QGP properties of interest as input parameters, is calibrated to fit the experimental data, thereby extracting a posterior probability distribution for the parameters.In this dissertation, I construct a specific computational model of heavy-ion collisions and formulate the Bayesian parameter estimation method, which is based on general statistical techniques.I then apply these tools to estimate fundamental QGP properties, including its key transport coefficients and characteristics of the initial state of heavy-ion collisions.Perhaps most notably, I report the most precise estimate to date of the temperature-dependent specific shear viscosity eta/s, the measurement of which is a primary goal of heavy-ion physics.The estimated minimum value is eta/s = 0.085(-0.025)(+0.026) (posterior median and 90% uncertainty), remarkably close to the conjectured lower bound of 1/4pi =~ 0.08.The analysis also shows that eta/s likely increases slowly as a function of temperature.Other estimated quantities include the temperature-dependent bulk viscosity zeta/s, the scaling of initial state entropy deposition, and the duration of the pre-equilibrium stage that precedes QGP formation.