Speaker
Andrzej Konrad Siodmok
(Jagiellonian University (PL))
Description
Encouraged by the success of applying machine learning techniques to the non-perturbative PDF problem, we decided to try the same in the context of non-perturbative hadronisation. In this presentation, I will show the first steps we took to construct HADML a Deep Generative Hadronization Model.
Then, I will describe the protocol we created to fit the Deep Generative Hadronization Model in a realistic setting where we only have access to the set of final stat hadrons in the data.
