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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Physical Review C

سال: 2023

ISSN: ['2470-0002', '2469-9985', '2469-9993']

DOI: https://doi.org/10.1103/physrevc.108.l011901