Information field based global Bayesian inference of the jet transport coefficient

TL;DR

This paper introduces an information field approach for Bayesian inference of the jet transport coefficient's temperature dependence, avoiding biases from explicit parametrizations, and applies it to heavy-ion collision data from RHIC and LHC.

Contribution

The paper develops an information field method for unbiased Bayesian inference of unknown functions and applies it to extract the temperature-dependent jet transport coefficient from experimental data.

Findings

The extracted $\hat q/T^3$ shows a strong temperature dependence.

The method avoids biases from specific functional forms.

Data constrains the function more as collision centrality and energy increase.

Abstract

Bayesian statistical inference is a powerful tool for model-data comparisons and extractions of physical parameters that are often unknown functions of system variables. Existing Bayesian analyses often rely on explicit parametrizations of the unknown function. It can introduce long-range correlations that impose fictitious constraints on physical parameters in regions of the variable space that are not probed by the experimental data. We develop an information field (IF) approach to modeling the prior distribution of the unknown function that is free of long-range correlations. We apply the IF approach to the first global Bayesian inference of the jet transport coefficient $\hat q$ as a function of temperature ($T$) from all existing experimental data on single-inclusive hadron, di-hadron and $\gamma$-hadron spectra in heavy-ion collisions at RHIC and LHC energies. The extracted $\hat q/T^3$ exhibits a strong $T$-dependence as a result of the progressive constraining power when data from more central collisions and at higher colliding energies are incrementally included. The IF method guarantees that the extracted $T$-dependence is not biased by a specific functional form.