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  • Letter
  • Open Access

Information-field-based global Bayesian inference of the jet transport coefficient

Man Xie1, Weiyao Ke2,3,4,*, Hanzhong Zhang1,†, and Xin-Nian Wang2,3,‡

  • 1Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, Wuhan 430079, China
  • 2Department of Physics, University of California, Berkeley, California 94720, USA
  • 3Nuclear Science Division MS 70R0319, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
  • 4Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

  • *weiyaoke@lanl.gov
  • †zhanghz@mail.ccnu.edu.cn
  • ‡xnwang@lbl.gov
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Phys. Rev. C 108, L011901 – Published 26 July, 2023

DOI: https://doi.org/10.1103/PhysRevC.108.L011901

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 q̂ as a function of temperature (T) from all existing experimental data on single-inclusive hadron, dihadron, and γ-hadron spectra in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider and CERN Large Hadron Collider energies. The extracted q̂/T3 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.

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Outline

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Phys. Rev. C 108, L011901– Published 26 July, 2023
  • Received 30 June 2022
  • Revised 29 April 2023
  • Accepted 7 June 2023
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.

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