Measuring jet quenching with a Bayesian inference analysis of hadron and jet data by JETSCAPE
/ Authors
R. Ehlers, A. Angerami, R. Arora, S. Bass, S. Cao, Y. Chen, L. Du, H. Elfner, W. Fan, R. Fries
and 43 more authors
C. Gale, Y. He, U. Heinz, B. Jacak, P. Jacobs, S. Jeon, Y. Ji, L. Kasper, M. Kordell, Abhishek Kumar, R. Kunnawalkam-Elayavalli, J. Latessa, S. Lee, Y.-J. Lee, D. Liyanage, M. Luzum, S. Mak, A. Majumder, A. Mankolli, C. Martin, H. Mehryar, T. Mengel, J. Mulligan, C. Nattrass, J. Paquet, C. Parker, J. Putschke, H. Roch, G. Roland, B. Schenke, L. Schwiebert, A. Sengupta, C. Shen, C. Sirimanna, D. Soeder, R. Soltz, I. Soudi, M. Strickland, Y. Tachibana, J. Velkovska, G. Vujanovic, X. Wang, W. Zhao
/ Abstract
The JETSCAPE Collaboration reports the first multi-messenger study of the QGP jet transport parameter q^ using Bayesian inference, incorporating all available hadron and jet inclusive yield and jet substructure data from RHIC and the LHC. The theoretical model utilizes virtuality-dependent in-medium partonic energy loss coupled to a detailed dynamical model of QGP evolution. Tension is observed when constraining q^ for different kinematic cuts of the inclusive hadron data. The addition of substructure data is shown to improve the constraint on q^, without inducing tension with the constraint due to inclusive observables. These studies provide new insight into the mechanisms of jet interactions in matter, and point to next steps in the field for comprehensive understanding of jet quenching as a probe of the QGP.
Journal: EPJ Web of Conferences