Constraining jet quenching in heavy-ion collisions with Bayesian inference

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 according to the universality of the quark/gluon quenching weights for different observables. We extract the color dependence of energy loss, which is observed to be bigger than what expected from Casimir scaling, showing a super-Casimir behaviour, and pointing to the importance of multi-parton quenching within high-p T jets at the LHC. The inclusion of nPDFs is shown to have a significant effect on the observables in study, particularly in the prediction of the modification factor R AA of photon-tagged jets, as well on the color dependence of energy loss.