Speaker
Manuel Szewc
Description
A fundamental part of event generation, hadronization is currently
simulated with the help of fine-tuned empirical models. In this talk,
I'll present MLHAD, a proposed alternative for hadronization where the
empirical model is replaced by a surrogate Machine Learning-based
model to be ultimately data-trainable. I'll detail the current stage
of development and discuss possible ways forward.
