Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

The Collaboration reports a new determination of the jet transport parameter <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mover accent="true"><a:mi>q</a:mi><a:mo>̂</a:mo></a:mover></a:math> in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published Bayesian inference determination of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"><c:mover accent="true"><c:mi>q</c:mi><c:mo>̂</c:mo></c:mover></c:math>, which was based solely on a selection of inclusive hadron suppression data. is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"><e:mover accent="true"><e:mi>q</e:mi><e:mo>̂</e:mo></e:mover></e:math> utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation.

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Publication Details

Journal
Physical review. C
Published
2025-05-15
DOI
https://doi.org/10.1103/physrevc.111.054913
Citations
9
Primary Topic
High-Energy Particle Collisions Research
Type
article
Field-Weighted Citation Impact
16.83

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article

Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

Y.-J. Lee, X.-N. Wang, Rashmi Datta, R. A. Soltz et al.
9 citations
Physical review. C
High-Energy Particle Collisions Research
16.83
article

Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

Y.-J. Lee, X.-N. Wang, Rashmi Datta, R. A. Soltz, Charles Gale, Abhijit Majumder, A. Angerami, Matthew Luzum, Joseph Latessa, M. Kordell, L. Schwiebert, Jean-François Paquet, J. H. Putschke, C. Nattrass, R. Arora, C. Sirimanna, Steffen A. Bass, Y. Chen, Hendrik Roch, Ismail Soudi, Yayun He, Chun Shen, C. De Martín, F. Jonas, Charles Thomas Parker, H. Mehryar, Sangyong Jeon, Lipei Du, Y. Tachibana, R. Lemmon, J. Velkovska, Andi Mankolli, Hannah Elfner, B. V. Jacak, Yi Ji, R. J. Fries, L. Kasper, T. Mengel, G. Vujanovic, D. Soeder, A. Sengupta, J. Norman, P. M. Jacobs, Wenbin Zhao, Mayank Singh, Simon Mak, James Declan Mulligan, G. Roland, Arun Kumar, R. J. Ehlers, B. Schenke, X. Wu, R. Kunnawalkam-Elayavalli
article en
9 citations

Abstract

The Collaboration reports a new determination of the jet transport parameter <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mover accent="true"><a:mi>q</a:mi><a:mo>̂</a:mo></a:mover></a:math> in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published Bayesian inference determination of <c:math xmlns:c="http://www.w3.org/1998/Math/MathML"><c:mover accent="true"><c:mi>q</c:mi><c:mo>̂</c:mo></c:mover></c:math>, which was based solely on a selection of inclusive hadron suppression data. is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of <e:math xmlns:e="http://www.w3.org/1998/Math/MathML"><e:mover accent="true"><e:mi>q</e:mi><e:mo>̂</e:mo></e:mover></e:math> utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation.

Physical review. CVol. 111(5)
Goethe University Frankfurt (DE), Lawrence Livermore National Laboratory (US), Goethe-Institute United Kingdom (GB), University of Liverpool (GB), Lawrence Berkeley National Laboratory (US), GSI Helmholtz Centre for Heavy Ion Research (DE), Duke University (US), Universidade de São Paulo (BR), Wayne State University (US), South China Normal University (CN), University of Regina (CA), Vanderbilt University (US), Brookhaven National Laboratory (US), Helsinki Institute of Physics (FI), Mitchell Institute (US), Frankfurt Institute for Advanced Studies (DE), Daresbury Laboratory (GB), Central China Normal University (CN), McGill University (CA), Massachusetts Institute of Technology (US), Akita International University (JP), University of Tennessee at Knoxville (US), Texas A&M University (US), University of Jyväskylä (FI), University of California, Berkeley (US)
National Science Foundation, U.S. Department of Energy, Wayne State University, University of Texas at Austin, University of Regina, San Diego Supercomputer Center, National Energy Research Scientific Computing Center, Canada Research Chairs, Fundação de Amparo à Pesquisa do Estado de São Paulo, National Natural Science Foundation of China, Academy of Finland, Office of Science, Office of the Vice President for Research, Wayne State University, Natural Sciences and Engineering Research Council of Canada, European Research Council, Nuclear Physics
Openalex Percentile: Top 1%
High-Energy Particle Collisions Research
16.83
DOIOpen Access PDFOpenAlex
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