Extrapolating Jet Radiation with Autoregressive Transformers
This addresses the need for fast and flexible event generation in high-energy physics, but it is incremental as it builds on existing generative network methods.
The paper tackled the problem of generating LHC events with variable numbers of particles using autoregressive transformers, showing that they can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets.
Generative networks are an exciting tool for fast LHC event generation. Usually, they are used to generate configurations with a fixed number of particles. Autoregressive transformers allow us to generate events with variable numbers of particles, very much in line with the physics of QCD jet radiation. We show how they can learn a factorized likelihood for jet radiation and extrapolate in terms of the number of generated jets. For this extrapolation, bootstrapping training data and training with modifications of the likelihood loss can be used.