Bayesian inference constraints on jet quenching across centrality, beam energy, and observable classes in LHC heavy-ion collisions

TL;DR

This study assesses the universality of Bayesian constraints on jet quenching parameters across different collision centralities, energies, and observables in LHC heavy-ion experiments, revealing partial compatibility and the need for diverse observables.

Contribution

It evaluates the transferability of Bayesian energy-loss model constraints across subsets of data, highlighting the importance of multiple observables for comprehensive jet quenching understanding.

Findings

Centrality-dependent posteriors are largely compatible.

Beam-energy and observable-class splits show moderate shifts.

Predictive performance varies when propagating subset posteriors to new datasets.

Abstract

Jet quenching in heavy-ion collisions probes parton energy loss in the quark--gluon plasma (QGP), but the extracted transport properties may not be universally constrained across centrality, beam energy, and observable class. In this work, we perform an analysis of the compatibility and predictive transferability of Bayesian constraints obtained from a six-parameter JETSCAPE effective energy-loss model across these subsets. The model is calibrated to charged-hadron and inclusive-jet data from ALICE, ATLAS, and CMS in PbPb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ and $2.76$ TeV. We find that centrality-dependent posteriors are largely compatible, whereas beam-energy and observable-class splits exhibit moderate shifts within overlapping credible regions, indicating that posterior overlap alone does not guarantee predictive universality. This is further examined by propagating subset posteriors to complementary datasets without refitting, where predictive performance varies across subsets. These results indicate that different observables probe distinct aspects of jet--medium interactions and motivate leading-hadron-selected jet observables to bridge hadron-biased and jet-inclusive constraints.