Speaker
Jonas Spinner
(Heidelberg University)
Description
Generative networks are promising tools for fast event generation for the LHC, yet struggle to meet the required precision when scaling up to particles in the final state. We employ the flexibility of autoregressive transformers to tackle this challenge, focusing on Z and top quark pair production with additional jets. We demonstrate the use of classifiers in combination with the autoregressive transformer to further improve the precision of the generated distributions.
