| Issue |
EPJ Web Conf.
Volume 296, 2024
30th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter 2023)
|
|
|---|---|---|
| Article Number | 15009 | |
| Number of page(s) | 4 | |
| Section | Small Systems | |
| DOI | https://doi.org/10.1051/epjconf/202429615009 | |
| Published online | 26 June 2024 | |
https://doi.org/10.1051/epjconf/202429615009
Measuring jet quenching with a Bayesian inference analysis of hadron and jet data by JETSCAPE
1 University of California, Berkeley
2 Lawrence Berkeley National Laboratory
* e-mail: raymond.ehlers@cern.ch
Published online: 26 June 2024
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.
© The Authors, published by EDP Sciences, 2024
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