Information field based global Bayesian inference of jet transport coefficient and dijet grandient tomography of heavy-ion collisions - INSPIRE

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Information field based global Bayesian inference of jet transport coefficient and dijet grandient tomography of heavy-ion collisions

Yayun He
(
- South China Normal U. and
- Cape Town U., Dept. Math.
)
,
Xin-Nian Wang
(
- LBNL, NSD
)
,
Man Xie
(
- South China Normal U. and
- Cape Town U., Dept. Math.
)

Feb 16, 2024

6 pages

Published in:

PoS HardProbes2023 (2024) 164

Contribution to:

- HP2023
, 164

Published: Feb 16, 2024

DOI:

10.22323/1.438.0164

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Abstract: (SISSA)

In this talk, two recent developments in jet tomography of heavy-ion collisions are discussed: (1) An information field approach to modeling the prior function distributions that is free of long-range correlations is applied to the first global Bayesian inference of the jet transport coefficient

\hat q

as a function of temperature (

T

) using global experimental data on single hadron, dihadron and

\gamma

-hadron spectra. 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. (2) Gradient jet tomography is extended to dijets in heavy-ion collisions. With fixed momentum of the leading jet, the transverse asymmetry of energetic hadrons from the subleading jet are shown to provide reasonable estimate of the dijet initial production points. This can be used to study geometric dependence of other jet observables such as jet-induced medium response

heavy ion: scattering
correlation: long-range
asymmetry: transverse
dijet
transport theory
Bayesian
hadron
temperature dependence
impact parameter
dihadron

References(20)

Figures(0)

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