Yayun He, Long-Gang Pang, and Xin-Nian Wang, Bayesian Extraction of Jet Energy Loss Distributions in Heavy-Ion Collisions, Phys. Rev. Lett. 122, 252302(2019)

High energy heavy ion collisions produce jets of energetic partons that traverse the strongly coupled quark-gluon plasma (sQGP). The interactions between jet-shower and medium partons lead to suppression of jets and large transverse momentum hadrons, in heavy-ion collisions as compared to proton-proton collisions. The suppression known as jet quenching can be used to probe multiple properties of the QGP, such as the temperature, the gluon density and jet transport coefficient.

In this study, the researchers formulate jet cross section in heavy-ion collisions as the convolution of jet cross section in proton-proton collisions and  the in-medium jet energy loss distribution. The trial jet energy loss distribution has three parameters whose functional space covers exponential distribution, Gamma distribution and gaussian distribution. The parameters of this statistical model for jet quenching is estimated through Bayesian analysis of experimental data from the Large Hadron Collider (LHC) with Markov Chain Monte Carlo (MCMC) method.

Results from Linear Boltzmann Transport (LBT) model simulations are consistent with the data-driven extraction  and indicate that the observed jet quenching prefers a small number of out-of-cone scatterings. Reducing experimental uncertainties in finer transverse momentum bins should improve the precision of the Bayesian extraction. Such systematic extraction of jet energy loss distributions can help to shed light on jet-medium interaction and constrain jet transport coefficients in high-energy heavy-ion collisions.

Figure:  Bayesian fits to RAA for single inclusive jets from ATLAS collaboration at LHC, (middle) the extracted average jet energy loss ⟨ΔpT⟩as a function of the initial jet energy and (bottom) energy loss distributions WAA(x=ΔpT/⟨ΔpT⟩) in Pb+Pb collisions at two LHC energies with different centralities. Blue lines with solid circles are mean averages from MCMC Bayesian fits and light blue lines are results with one sigma deviation from the average fits of RAA. Red lines are from LBT simulations.