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Multi-Observable Analysis of Jet Quenching Using Bayesian Inference

October 22 (in person) Raymond Ehlers (LBNL) Host: Peter Jacobs Title: Multi-Observable Analysis of Jet Quenching Using Bayesian Inference Abstract: Bayesian inference is a flexible and incisive tool for determining underlying physical parameters, as well as characterizing consistency between data and models. It has been successfully applied to investigations of the quark-gluon plasma (QGP), utilizing a variety of measurements from RHIC and LHC. I will discuss a new multi-observable study of jet transport in the QGP using Bayesian Inference [1], for the first time incorporating all available inclusive hadron and jet suppression data. This study extends the previous JETSCAPE Bayesian Inference jet quenching analysis, which was based solely on selected inclusive hadron data. The multi-observable nature of the analysis enables exploration of correlations and differences between different probes and different kinematic ranges. Notably, tension is observed between calibrations based on hadron RAA for pT less than 30 GeV/c, and higher pT hadron and jet RAA data. We test the consistency of the theoretical formulation over a wide range of jet quenching data, and identifying those aspects of the formulation that are in tension with data. I will further discuss a first look at the inclusion of jet substructure measurements, as well as new methods and measurements for future Bayesian inference studies towards a comprehensive understanding of jet interactions in the QGP. [1]: JETSCAPE Collaboration, arXiv: 2408.08247