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A new metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression
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abstract
We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium DGLAP evolution at high virtuality, and (linearized) Boltzmann Transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high $p_T$ charged hadrons, D-mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient $\hat{q}$. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses.
Forward citations
Cited by 2 Pith papers
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Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference
A Bayesian fit to LHC jet data claims a universal jet energy-loss distribution and super-Casimir color dependence, but the color result depends on a theory-informed prior.
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Hybrid Hadronization -- A Study of In-Medium Hadronization of Jets
Hybrid Hadronization converts a substantial part of jet fragmentation in a QGP brick into shower-thermal recombination, which grows with medium size, carries collective flow, and boosts baryon/meson ratios.
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