2020/02/13 by Jean-François Paquet, J. -F. Paquet, A. Angerami +48 · 28 citations
Mathematics · Physics and Astronomy · #Artificial intelligence #Bayesian econometrics #Bayesian inference #Bayesian probability #Bayesian statistics #Closure (psychology) #Collision #Computer science #Context (archaeology) #Econometrics #Economics #Geology #Hadron #Heavy ion #High-Energy Particle Collisions Research #Ion #Machine learning #Mathematics #Particle physics #Particle physics theoretical and experimental studies #Physics #Proxy (statistics) #Quantum Chromodynamics and Particle Interactions #Quantum chromodynamics #Statistical physics #hep-ph #nucl-th
paper · pdf · doi:10.1016/j.nuclphysa.2020.121749
published in Nuclear Physics A 1005, 121749 (Elsevier BV) · 4 pages, 1 figure, contribution to the Quark Matter 2019 proceedings
arxiv created 2020/02/13 · openalex publication_date 2020/12/11 · arxiv updated 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Multistage models based on relativistic viscous hydrodynamics have proven successful in describing hadron measurements from relativistic nuclear collisions. These measurements are sensitive to the shear and the bulk viscosities of QCD and provide a unique opportunity to constrain these transport coefficients. Bayesian analyses can be used to obtain systematic constraints on the viscosities of QCD, through methodical model-to-data comparisons. In this manuscript, we discuss recent developments in Bayesian analyses of heavy ion collision data. We highlight the essential role of closure tests in validating a Bayesian analysis before comparison with measurements. We discuss the role of the emulator that is used as proxy for the multistage theoretical model. We use an ongoing Bayesian analysis of soft hadron measurements by the JETSCAPE Collaboration as context for the discussion.