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

Xie, Man; Ke, Weiyao; Zhang, Hanzhong; Wang, Xin-Nian

doi:10.1103/PhysRevC.108.L011901

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

Man Xie
(
- CCNU, Wuhan, Inst. Part. Phys. and
- Hua-Zhong Normal U., LQLP
)
,
Weiyao Ke
(
- UC, Berkeley and
- LBNL, NSD and
- Los Alamos
)
,
Hanzhong Zhang
(
- CCNU, Wuhan, Inst. Part. Phys. and
- Hua-Zhong Normal U., LQLP
)
,
Xin-Nian Wang
(
- UC, Berkeley and
- LBNL, NSD
)

Jun 2, 2022

7 pages

Published in:

Phys.Rev.C 108 (2023) 1, L011901

Published: Jul 26, 2023

e-Print:

2206.01340 [hep-ph]

DOI:

10.1103/PhysRevC.108.L011901 (publication)

Report number:

LA-UR-22-25147

View in:

pdf

reference search41 citations

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Abstract: (APS)

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, dihadron, and

γ

-hadron spectra in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider and CERN Large Hadron Collider 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.

Note:

7 pages in RevTex with 4 figures

correlation: long-range
heavy ion: scattering
Bayesian
transport theory
temperature dependence
parametrization
CERN LHC Coll
Brookhaven RHIC Coll
statistical
impact parameter

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