New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

This is the authors' abstract. We don't add key points for this paper.

We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high-<a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msub><a:mi>p</a:mi><a:mi>T</a:mi></a:msub></a:math> charged hadrons, <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mi>D</b:mi></b:math> mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"><c:mover accent="true"><c:mi>q</c:mi><c:mo>̂</c:mo></c:mover></c:math>. 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.