Towards a Deep Learning Model for Hadronization

Understand

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 Unit (GPUs).