Speaker: Sunil Jaiswal, The Ohio State University

Experiments at the LHC and RHIC provide a wealth of data for studying the properties of nuclear matter. To analyze this data, computationally demanding multi-stage physics models are required to simulate different stages of nuclear evolution. To reduce computation time, model surrogates, also known as emulators, are employed, which are essential for Bayesian studies in heavy-ion physics. In the first part of my talk, I will introduce new Gaussian Process emulators that are especially useful for these simulations. In the second part, I will discuss the importance of quantifying theoretical uncertainties when comparing physics models with experimental data. I will present results from earlier Bayesian analyses of heavy-ion collisions, showing how differences in inferred physical parameters, like shear and bulk viscosities, can arise in different studies. Finally, I will use a simple ball drop experiment to illustrate how considering theoretical uncertainties can help resolve these differences.