Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference

TL;DR

This paper uses Bayesian inference to analyze jet quenching in heavy-ion collisions, revealing universal quenching weights and a slightly stronger color dependence than Casimir scaling, enhancing understanding of energy loss mechanisms.

Contribution

It introduces a data-driven Bayesian approach with flexible parametrizations to extract jet energy-loss distributions and tests their universality across different datasets.

Findings

Evidence for universal quark/gluon quenching weights

Color dependence of energy loss slightly exceeds Casimir scaling

Consistency established between different experimental datasets

Abstract

Jet suppression and modification is a hallmark feature of heavy-ion collisions. This can be attributed to an accumulated set of effects, including radiative and elastic energy loss and reabsorption of thermalized energy within the jet cone, which are encoded in a quenching weight, determining the probability distribution for a shift of the $p_T$ (energy loss). We perform a data-driven analysis, based on Bayesian inference, to extract information about the energy-loss distribution experienced by propagating jets using generic and flexible parametrizations. We first establish the consistency between different data-sets and, thereby, provide evidence for the universality of the quark/gluon quenching weights for different observables. Furthermore, we extract that the color dependence of energy loss is slightly bigger than what expected from Casimir scaling, pointing to the importance of multi-parton quenching within high-$p_T$ jets at the LHC.