Extrapolating jet radiation with autoregressive transformers
Abstract
Generative networks are an exciting tool for fast LHC event generation with fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet radiation, and offer the possibility to generalize to higher multiplicities. We show how transformers 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.
Fields of Study & Subjects
Particle physics theoretical and experimental studies
Computational Physics and Python Applications
Algorithms and Data Compression
Publication details
- Journal / source
- SciPost Physics
- Publisher
- Stichting SciPost
- Volume
- 20
- Issue
- 1
- Publication date
- 12 January 2026
- Language
- EN