Speaker
Description
Over the past decade, Bayesian inference has become an invaluable tool in heavy-ion collision modeling. By integrating input from both experimental and theoretical perspectives, it bridges two traditionally separate domains. Progress in this framework relies on both high-precision experimental measurements and theoretical developments that yield more accurate predictions. This dual requirement positions Bayesian analyses as a collaborative frontier, offering opportunities for both experimentalists and theorists.
The recent availability of high-precision measurements paves the way for broader and more thorough studies. Historically, Bayesian inference has primarily been used to investigate the transport properties of the quark-gluon plasma (QGP) produced in heavy-ion collisions, successfully constraining the temperature dependence of specific shear and bulk viscosities. More recent studies have extended the method to explore initial-stage dynamics, nuclear structure, and jet quenching.
In this talk, I will discuss the current and future roles of Bayesian inference, highlighting its advantages and limitations. Finally, I will outline how we can further develop and refine the framework to achieve a more consistent and comprehensive understanding of QGP matter.
