Bayesian extraction of jet energy loss distributions in heavy-ion collisions

TL;DR

This paper uses Bayesian analysis and Markov Chain Monte Carlo methods to extract jet energy loss distributions from heavy-ion collision data, revealing their dependence on initial jet energy and collision centrality.

Contribution

It introduces a Bayesian framework for extracting jet energy loss distributions from experimental data, incorporating the factorization in perturbative QCD and MCMC techniques.

Findings

Jet energy loss depends slightly more than logarithmically on initial jet energy.

Jet energy loss decreases from central to peripheral collisions.

Extracted distributions have a large width and are consistent with Boltzmann transport model simulations.

Abstract

Based on the factorization in perturbative QCD, a jet cross sections 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 $Pb+Pb$ 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 $x=\Delta p_T /\langle \Delta p_T\rangle$ 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.