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

dc.contributor.advisor

Bass, Steffen A.

dc.contributor.author

Xu, Yingru

dc.date.accessioned

2020-01-27T16:52:21Z

dc.date.available

2020-01-27T16:52:21Z

dc.date.issued

2019

dc.department

Physics

dc.description.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.

dc.identifier.uri

https://hdl.handle.net/10161/19844

dc.subject

Physics

dc.subject

Bayesian

dc.subject

data-driven

dc.subject

heavy quark

dc.subject

transport coefficients

dc.title

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

dc.type

Dissertation

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Xu_duke_0066D_15318.pdf
Size:
11.72 MB
Format:
Adobe Portable Document Format

Collections