Towards a Deeper Understanding of How Experiments Constrain the Underlying Physics of Heavy-Ion Collisions
Evan Sangaline, Scott Pratt

TL;DR
This paper introduces a new Bayesian analysis method to better understand how experimental data constrains the physics of heavy-ion collisions, helping identify key measurements and model limitations.
Contribution
It extends Bayesian Markov Chain Monte Carlo techniques to quantitatively analyze how experimental observables influence model parameter inferences in heavy-ion collision studies.
Findings
Identifies which experimental measurements most strongly constrain model parameters.
Highlights relationships between observables and underlying physics.
Provides insights into model weaknesses and future experimental priorities.
Abstract
Recent work has provided the means to rigorously determine properties of super-hadronic matter from experimental data through the application of broad scale modeling of high-energy nuclear collisions within a Bayesian framework. These studies have provided unprecedented statistical inferences about the physics underlying nuclear collisions by virtue of simultaneously considering a wide range of model parameters and experimental observables. Notably, this approach has been used to constrain both the QCD equation of state and the shear viscosity above the quark-hadron transition. Although the inferences themselves have a clear meaning, the complex nature of the relationships between model parameters and observables have remained relatively obscure. We present here a novel extension of the standard Bayesian Markov Chain Monte Carlo approach that allows for the quantitative determination of…
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