Fitting a Deep Generative Hadronization Model

Understand

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments.

• As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data.
• Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision.
• Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods.