Bayesian parameter estimation for relativistic heavy-ion collisions
Jonah E. Bernhard

TL;DR
This paper develops a Bayesian framework to estimate properties of the quark-gluon plasma in heavy-ion collisions, providing the most precise measurement of its shear viscosity to date and insights into its initial state and evolution.
Contribution
It introduces a Bayesian parameter estimation method applied to a computational collision model, yielding new precise estimates of QGP properties, especially the temperature-dependent shear viscosity.
Findings
Most precise estimate of shear viscosity to date: η/s ≈ 0.085 with uncertainties.
Evidence that η/s increases slowly with temperature.
Estimated initial state entropy scaling and pre-equilibrium duration.
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 meters in size and living 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…
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Particle physics theoretical and experimental studies · Quantum Chromodynamics and Particle Interactions
