Speaker
Description
The path toward a definitive characterization of the Quark-Gluon Plasma (QGP) requires a simultaneous, global analysis of all available jet modification data. The JETSCAPE Collaboration presents a landmark, curated dataset, the most comprehensive to date, that unifies the entirety of available jet and high transverse momentum hadron measurements from the LHC and RHIC programs into a common format. Consisting of over 90 observables and thousands of data points, this dataset represents a foundational effort to centralize experimental results for an unbiased global fit. To enable rigorous theory-to-data comparisons, we introduce a user-friendly and extensible Bayesian inference framework powered by a machine-learning-enhanced pipeline. This infrastructure enables the rigorous treatment of systematic uncertainties, specifically addressing the complex correlations of similar measurements within and across different experimental collaborations. We demonstrate the capabilities of this framework with a new global extraction of the jet transport coefficient, $\hat{q}$, including jet-substructure observables for the first time. This framework promises unprecedented constraints on $\hat{q}$ by incorporating all available data. It serves as a new benchmark for the field and establishes a common analysis infrastructure for future high-precision QGP studies, enabling the rigorous extraction of fundamental QCD parameters and transport coefficients across diverse theoretical frameworks.
| Is this an experimental talk? | No |
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| Is this on behalf of a collaboration? | Yes |
| Which collaboration? | JETSCAPE |
| Are you willing to present as a poster if it is not selected for oral presentation? | Yes |