3 citations · 5 across the 2 of their papers we have counts for
2 papers
hep-ph2023★ 3 cited
Integrating Particle Flavor into Deep Learning Models for Hadronization
Jay Chan, Xiangyang Ju, Adam Kania +3
Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there hav…
hep-ph2023★ 2 cited
Fitting a Deep Generative Hadronization Model
Jay Chan, Xiangyang Ju, Adam Kania +3
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, physicall…