Measuring jet quenching with a Bayesian inference analysis of hadron and jet data by JETSCAPE

• DOI: 10.48550/arxiv.2401.04201
• Publication Date: 2024-01-01
• Authors: R. J. Ehlers; A. Angerami; R. Arora; Steffen A. Bass; Shanshan Cao; Y. Chen; Lipei Du; Hannah Elfner; Wenkai Fan; Rainer J. Fries; Charles Gale; Yayun He; Ulrich Heinz; B. V. Jacak; P. M. Jacobs; Sangyong Jeon; Y. Ji; L. Kasper; M. Kordell; Amit Kumar; R. Kunnawalkam El...; J. Latessa; Seongmin Lee; Y. -J. Lee; D. Liyanage; Matthew Luzum; Simon Mak; Abhijit Majumder; A. Mankolli; C. Martin; H. Mehryar; T. Mengel; James Declan Mull...; C. Nattrass; J. -F. Paquet; Charles Thomas Pa...; J. H. Putschke; Hendrik Roch; G. Roland; B. Schenke; L. Schwiebert; A. Sengupta; Chun Shen; C. Sirimanna; D. Soeder; R. A. Soltz; Ismail Soudi; M. Strickland; Y. Tachibana; J. Velkovska; Gojko Vujanovic; X. -N. Wang; W. Zhao
• Keywords: Jet quenching; Substructure

The JETSCAPE Collaboration reports the first multi-messenger study of QGP jet transport parameter $\hat{q}$ using Bayesian inference, incorporating all available hadron and inclusive yield substructure data from RHIC LHC. theoretical model utilizes virtuality-dependent in-medium partonic energy loss coupled to a detailed dynamical evolution. Tension is observed when constraining for different kinematic cuts data. addition shown improve constraint on $\hat{q}$, without inducing tension with due observables. These studies provide new insight into mechanisms interactions in matter, point next steps field comprehensive understanding quenching as probe QGP.