Towards a deep learning model for hadronization
A. Ghosh, X. Ju, Benjamin Nachman, Andrzej Siódmok
Abstract
Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely used models of hadronization in event generators are based on physically inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with graphical processing units. We make the first step towards a data-driven machine learning-based hadronization model. In that step, we replace a component of the hadronization model within the Herwig event generator (cluster model) with $\mathsf{HADML}$, a computer code implementing a generative adversarial network. We show that a $\mathsf{HADML}$ is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate it into $\mathsf{Herwig}$ to generate entire events that can be compared with the output of the public $\mathsf{Herwig}$ simulator as well as with ${e}^{+}{e}^{\ensuremath{-}}$ data.