  In nuclear and particle physics, reconciling sophisticated simulations with
experimental data is vital for understanding complex systems like the Quark
Gluon Plasma (QGP) generated in heavy-ion collisions. However, computational
demands pose challenges, motivating using Gaussian Process emulators for
efficient parameter extraction via Bayesian calibration. We conduct a
comparative analysis of Gaussian Process emulators in heavy-ion physics to
identify the most adept emulator for parameter extraction with minimal
uncertainty. Our study contributes to advancing computational techniques in
heavy-ion physics, enhancing our ability to interpret experimental data and
understand QGP properties.
