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 $1/4\pi \simeq 0.08$. 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.