Bayesian Extraction of Jet Energy Loss Distributions in Heavy-Ion Collisions.

Yayun He,Xin-Nian Wang,Long-Gang Pang

doi:10.1103/physrevlett.122.252302

Abstract

Based on the factorization in perturbative QCD, a jet cross section in heavy-ion collisions can be expressed as a convolution of the jet cross section in p+p collisions and a jet energy loss distribution. Using this simple expression and the Markov Chain MonteCarlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and γ-triggered jets in Pb+Pb collisions with different centralities at two colliding energies at the Large Hadron Collider. The average jet energy loss has a dependence on the initial jet energy that is slightly stronger than a logarithmic form and decreases from central to peripheral collisions. The extracted jet energy loss distributions with a scaling behavior in x=Δp_{T}/⟨Δp_{T}⟩ have a large width. These are consistent with the linear Boltzmann transport model simulations, in which the observed jet quenching is caused on the average by only a few out-of-cone scatterings.

Highlights

Based on the factorization in perturbative QCD, a jet cross section in heavy-ion collisions can be expressed as a convolution of the jet cross section in p þ p collisions and a jet energy loss distribution. Using this simple expression and the Markov Chain Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and γ-triggered jets in Pb þ Pb collisions with different centralities at two colliding energies at the Large Hadron Collider
In this Letter, we first show that starting from the factorized form of jet cross section, the jet production cross section in heavy-ion collisions can be expressed as the convolution of cross section in proton-proton collisions and a flavor-averaged jet energy loss distribution. Based on this simple expression, we use the Markov Chain Monte Carlo (MCMC) method [22] to carry out the first Bayesian analyses of experimental data on the medium modification of both single inclusive and γ-triggered jet spectra and extract jet energy loss distributions in heavy-ion collisions at two colliding energies at the Large Hadron Collider (LHC) with different centralities
Bayesian analyses with MCMC.—The focus of the rest of this Letter is to use Bayesian analyses of experimental data on both single inclusive and γ-triggered jet spectra in p þ p and A þ A collisions to extract the jet energy loss distribution WAA using the convolution expression in Eq (6)

Summary

Introduction

Using this simple expression and the Markov Chain Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and γ-triggered jets in Pb þ Pb collisions with different centralities at two colliding energies at the Large Hadron Collider. Among many efforts to extract the jet transport coefficient from experimental data on suppression of single inclusive hadron spectra [6,7,8,9], the systematic study by the JET Collaboration [10] has narrowed the uncertainties to within 40%.

Results

Conclusion