On model emulation and closure tests for 3+1D relativistic heavy-ion collisions

Abstract

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.

21 pages, 13 figures

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Cited by in corpus (6)

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• Global Bayesian Analysis of $\mathrm{J}/\psi$ Photoproduction on Proton and Lead Targets
• Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions
• A Gaussian Process Generative Model for QCD Equation of State
• Learning Informed Prior Distributions with Normalizing Flows for Bayesian Analysis
• Neural network enhanced Bayesian global analysis of relativistic heavy ion collisions