Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements
R. Ehlers (Department of Physics, University of California, Berkeley, California 94270, USA, Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94270, USA); Y. Chen (Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA, Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA, Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, USA); J. Mulligan (Department of Physics, University of California, Berkeley, California 94270, USA, Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94270, USA); Y. Ji (Department of Statistical Science, Duke University, Durham, North Carolina 27708, USA); A. Kumar (Department of Physics and Astronomy, Wayne State University, Detroit, Michigan 48201, USA, Department of Physics, University of Regina, Regina, Saskatchewan S4S 0A2, Canada, Department of Physics, McGill University, Montréal, Québec H3A 2T8, Canada); et al - Show all 53 authors
The JETSCAPE Collaboration reports a new determination of the jet transport parameter 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 JETSCAPE Bayesian inference determination of , which was based solely on a selection of inclusive hadron suppression data. jetscape 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 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.
