Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging

Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ∼150-350  MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints f...

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Main Authors: Roland, Gunther, Chen, Y.
Other Authors: Massachusetts Institute of Technology. Laboratory for Nuclear Science
Format: Article
Language:English
Published: American Physical Society (APS) 2022
Online Access:https://hdl.handle.net/1721.1/142131
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author Roland, Gunther
Chen, Y.
author2 Massachusetts Institute of Technology. Laboratory for Nuclear Science
author_facet Massachusetts Institute of Technology. Laboratory for Nuclear Science
Roland, Gunther
Chen, Y.
author_sort Roland, Gunther
collection MIT
description Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ∼150-350  MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints for the viscosities. With Bayesian model averaging we propagate an estimate of the model uncertainty generated by the transition from hydrodynamics to hadron transport in the plasma's final evolution stage, providing the most reliable phenomenological constraints to date on the QGP viscosities.
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spelling mit-1721.1/1421312023-01-24T21:21:07Z Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging Roland, Gunther Chen, Y. Massachusetts Institute of Technology. Laboratory for Nuclear Science Massachusetts Institute of Technology. Department of Physics Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ∼150-350  MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints for the viscosities. With Bayesian model averaging we propagate an estimate of the model uncertainty generated by the transition from hydrodynamics to hadron transport in the plasma's final evolution stage, providing the most reliable phenomenological constraints to date on the QGP viscosities. 2022-04-27T15:42:19Z 2022-04-27T15:42:19Z 2021 2022-04-27T15:29:32Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/142131 Roland, Gunther. 2021. "Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging." Physical Review Letters, 126 (24). en 10.1103/PHYSREVLETT.126.242301 Physical Review Letters Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf American Physical Society (APS) APS
spellingShingle Roland, Gunther
Chen, Y.
Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title_full Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title_fullStr Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title_full_unstemmed Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title_short Phenomenological Constraints on the Transport Properties of QCD Matter with Data-Driven Model Averaging
title_sort phenomenological constraints on the transport properties of qcd matter with data driven model averaging
url https://hdl.handle.net/1721.1/142131
work_keys_str_mv AT rolandgunther phenomenologicalconstraintsonthetransportpropertiesofqcdmatterwithdatadrivenmodelaveraging
AT cheny phenomenologicalconstraintsonthetransportpropertiesofqcdmatterwithdatadrivenmodelaveraging