Speaker
Jonas Spinner
Description
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. Beyond particle physics applications, our studies show that autoregressive transformers can extrapolate.
References
https://arxiv.org/abs/2412.12074
Authors
Ayodele Ore
Javier Mariño Villadamigo
(Institut für Theoretische Physik - University of Heidelberg)
Jonas Spinner
Tilman Plehn
