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Recent work has provided the means to rigorously determine properties ofsuper-hadronic matter from experimental data through the application of broadscale modeling of high-energy nuclear collisions within a Bayesian framework.These studies have provided unprecedented statistical inferences about thephysics underlying nuclear collisions by virtue of simultaneously considering awide range of model parameters and experimental observables. Notably, thisapproach has been used to constrain both the QCD equation of state and theshear viscosity above the quark-hadron transition. Although the inferencesthemselves have a clear meaning, the complex nature of the relationshipsbetween model parameters and observables have remained relatively obscure. Wepresent here a novel extension of the standard Bayesian Markov Chain MonteCarlo approach that allows for the quantitative determination of how inferencesof model parameters are driven by experimental measurements and theiruncertainties. This technique is then applied in the context of heavy ioncollisions in order to explore previous results in greater depth. The resultingrelationships are useful for identifying model weaknesses, prioritizing futureexperimental measurements, and most importantly: developing an intuition forthe role that different observables play in constraining our understanding ofthe underlying physics.

Towards a Deeper Understanding of How Experiments Constrain the Underlying Physics of Heavy-Ion Collisions