Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning
Published in arXiv, 2023
In ultrarelativistic heavy-ion collisions, a plasma of deconfined quarks and gluons is formed within 1 fm/c of the nuclei’s impact. The complex dynamics of the collision before 1 fm/c is often described with parametric models, which affect the predictivity of calculations. In this work, we perform a systematic analysis of LHC measurements from Pb-Pb collisions, by combining an \emph{ab-initio} model of the early stage of the collisions with a hydrodynamic model of the plasma. We obtain state-of-the-art constraints on the shear and bulk viscosity of quark-gluon plasma. We mitigate the additional cost of the ab-initio initial conditions by combining Bayesian model averaging with transfer learning, allowing us to account for important theoretical uncertainties in the hydrodynamics-to-hadron transition. We show that, despite the apparent strong constraints on the shear viscosity, metrics that balance the model’s predictivity with its degree of agreement with data do not prefer a temperature-dependent specific shear viscosity over a constant value. We validate the model by comparing with discriminating observables not used in the calibration, finding excellent agreement.
Recommended citation: M. Heffernan, C. Gale, S. Jeon, and J.-F. Paquet, (2023). "Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning." arXiv preprint 2306.09619
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