Nuclear Physics Seminar - Chun Shen (Wayne State) "Bayesian Inference in Relativistic Heavy-Ion Collisions"

Relativistic heavy-ion collisions provide a unique opportunity to study quantum chromodynamics (QCD) under extreme conditions and to characterize the properties of the quark-gluon plasma (QGP). However, extracting underlying QGP properties from experimental measurements remains a major challenge because of complex collision dynamics and multi-stage, nonequilibrium evolution with many interconnected model parameters.

In this seminar, I will discuss the development and application of Bayesian inference as a systematic framework for extracting QGP properties from experimental data. I will highlight recent developments in Bayesian analyses of the Relativistic Heavy Ion Collider Beam Energy Scan program and high-energy collisions at the Large Hadron Collider, emphasizing the role of rapidity-dependent observables in probing the longitudinal dynamics of the collision system and the properties of baryon-rich QCD matter. Finally, I will discuss emerging directions in Bayesian model calibration, including nonparametric inference of temperature-dependent QGP properties, uncertainty quantification, and the integration of advanced machine-learning techniques with high-performance computing. These developments are paving the way toward a more precise and comprehensive understanding of QCD matter across a broad range of temperatures and baryon densities.