Bayesian Parameter Estimation for Relativistic Heavy-ion Collisions

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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.






Bernhard, Jonah (2018). Bayesian Parameter Estimation for Relativistic Heavy-ion Collisions. Dissertation, Duke University. Retrieved from


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