# Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements

**Authors:** R. Ehlers, Y. Chen, J. Mulligan, Y. Ji, A. Kumar, S. Mak, P. M. Jacobs, A. Majumder, A. Angerami, R. Arora, S. A. Bass, R. Datta, L. Du, H. Elfner, R. J. Fries, C. Gale, Y. He, B. V. Jacak, S. Jeon, F. Jonas  
**arXiv:** [2408.08247](https://arxiv.org/abs/2408.08247)  
**Date:** 2024-08-15  
**Summary page:** https://www.summarizepaper.com/en/paper/2408.08247v1/

## Key points

- The JETSCAPE Collaboration conducted a Bayesian Inference analysis of jet quenching in the Quark-Gluon Plasma (QGP) to determine the jet transport parameter $\hat{q$.
- The study incorporated inclusive hadron and jet yield suppression data from heavy-ion collisions at RHIC and the LHC, providing a more detailed understanding of $\hat{q$.
- The researchers utilized the JETSCAPE framework to model partonic energy loss in the QGP as a thermalized weakly-coupled plasma, with parameters determined through Bayesian calibration using soft-sector observables.
- Active Learning techniques were used to enhance computational efficiency during the calibration process.
- Various calibrations were performed based on different combinations of jet and hadron data, as well as variations in kinematic or centrality ranges within the datasets, revealing tensions that offer insights into jet transport in the QGP and its theoretical formulation.

## Summary

The JETSCAPE Collaboration presents a groundbreaking Bayesian Inference analysis of jet quenching in the Quark-Gluon Plasma (QGP), focusing on the determination of the jet transport parameter $\hat{q}$. This study incorporates a comprehensive set of inclusive hadron and jet yield suppression data obtained from heavy-ion collisions at both RHIC and the LHC. Building upon their previous work, which was primarily based on inclusive hadron suppression data, this multi-observable analysis provides a more robust and detailed understanding of $\hat{q}$. Utilizing the versatile JETSCAPE framework, which integrates sophisticated dynamical models of QGP formation and evolution, as well as jet propagation and interaction within the QGP, the researchers model virtuality-dependent partonic energy loss in the QGP as a thermalized weakly-coupled plasma. The parameters governing this process are determined through Bayesian calibration using soft-sector observables. Notably, Active Learning techniques are employed to enhance computational efficiency during the calibration process. The experimental dataset considered in this analysis encompasses a wide range of collision energies, centrality classes, and transverse momentum values. To systematically explore the dependence of the extracted parameter posterior distributions, various calibrations are performed based on different combinations of jet and hadron data, as well as variations in kinematic or centrality ranges within the datasets. The comparison of these different calibrations reveals tensions that offer valuable insights into the underlying physics of jet transport in the QGP and its theoretical formulation. Overall, this refined analysis by  represents a significant advancement in our understanding of  phenomena in high-energy heavy-ion collisions, shedding light on key aspects of  and providing crucial constraints for theoretical models in this complex field of research.