Data-driven Analysis of Heavy Quark Transport in Ultra-relativistic Heavy-ion Collisions

Loading...
Thumbnail Image

Date

2019

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

193
views
77
downloads

Abstract

Heavy flavor observables provide valuable information on the properties of the hot and dense Quark-Gluon Plasma (QGP) created in ultra-relativistic heavy-ion collisions.

Previous study has made significant progress regarding the heavy quark in-medium interaction, energy loss and collective behaviors. Various theoretical models are developed to describe the evolution of heavy quarks in heavy-ion collisions, but also show limited performance as they experience challenges to simultaneously describe all the experimental data.

In this thesis, I present a state-of-the-art Bayesian model-to-data analysis to calibrate a heavy quark evolution model on the experimental data at different collision systems and different energies: the heavy quark evolution model incorporates an improved Langevin dynamics for heavy quarks with an event-by-event viscous hydrodynamical model for the expanding QGP medium, and considers both heavy quark collisional and radiative energy loss. By applying the Bayesian analysis to such a modularized framework, the heavy quark evolution model is able to describe the heavy flavor observables in multiple collision system and make prediction of unseen observables. In addition, the estimated heavy quark diffusion coefficient shows a strong positive temperature dependence and strong interaction around the critical temperature.

Finally, by comparing the transport coefficients estimated by various theoretical approaches, I have quantitatively evaluated the contribution from different sources of deviation, which can provide a reference for the theoretical uncertainties regarding the heavy quark transport coefficients.

Department

Description

Provenance

Citation

Citation

Xu, Yingru (2019). Data-driven Analysis of Heavy Quark Transport in Ultra-relativistic Heavy-ion Collisions. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/19844.

Collections


Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.