Speaker
Stephen Mrenna
(Fermilab)
Description
Modelling the transition from free to bound partons, the process of hadronization, intersects perturbative and phenomenological methods. The Lund string model has successfully predicted any number of hadronization phenomena over the years, yet remains deficient in a number of observables. The MLhad collaboration is using machine learned models to augment the string model and provide deeper interpretations of this phenomenology. Here, we demonstrate how these models can be learned from data, as well as a number of technical advancement in the Pythia 8 event generator allowing for rapid exploration of model parameter space.
