3 citations · 3 across the 6 of their papers we have counts for
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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
Event Generator Tuning Incorporating Systematic Uncertainty
Jaffae Schroff, Xiangyang Ju
Event generators play an important role in all physics programs at the Large Hadron Collider and beyond. Dedicated efforts are required to tune the parameters of event generators t…
hep-ph2023
Simulation of Hadronic Interactions with Deep Generative Models
Tuan Minh Pham, Xiangyang Ju
Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadroni…