Speaker
Jonas Spinner
Description
Generative networks are promising tools in fast event generation for the LHC, yet struggle to meet the required precision when scaling up to large multiplicities. We employ the flexibility of autoregressive transformers to tackle this challenge, focusing on Z and top quark pair production with additional jets. In order to further increase precision, we use classifiers to reweight the generated distributions.
