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

TL;DR

This paper introduces a new metric for validating Bayesian calibration workflows in a multistage approach to studying parton interactions with quark-gluon plasma, improving model-data comparison and parameter sensitivity analysis.

Contribution

It develops a quantitative metric for validating Bayesian workflows, enhancing the calibration of a multistage parton energy loss model against experimental data.

Findings

The new metric improves Bayesian model emulation accuracy.

Calibration of the transport coefficient $tq$ aligns well with experimental data.

Sensitivity analysis reveals key parameters influencing observables.

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.