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

He, Yayun (Key Laboratory of Quark & Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China) (Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) ; Pang, Long-Gang (Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) (Physics Department, University of California, Berkeley, California 94720, USA) ; Wang, Xin-Nian (Key Laboratory of Quark & Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China) (Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA) (Physics Department, University of California, Berkeley, California 94720, USA)

27 June 2019

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 Monte Carlo method, we carry out Bayesian analyses of experimental data on jet spectra to extract energy loss distributions for both single inclusive and $\gamma$-triggered jets in $\mathrm{Pb}+\mathrm{Pb}$ 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 $x=\Delta {p}_{T}/⟨\Delta {p}_{T}⟩$ 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.

Published in: Physical Review Letters 122 (2019)