Speaker
Manuel Szewc
Description
Hadronization, the transition between unobservable quarks and gluons to observable hadrons, is a key aspect of the theoretical framework of particle physics. However, it is a fundamentally challenging process due to its non-pertubative nature, and thus event generators implement empirical models based on QCD insights. In this talk, I'll detail how Machine Learning has been incorporated into different aspects of hadronization modeling, including parameter tuning uncertainty quantification, and ML-based models, with an emphasis on ongoing challenges and possible ways forward.