Extrapolating Jet Radiation with Autoregressive Transformers

2024/12/16 by Anja Butter, Butter, Anja, François Charton +9 · 1 voice · 4 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #cs.LG #hep-ph

paper · pdf · doi:10.48550/arxiv.2412.12074

arxiv published 2024/12/16 · arxiv updated 2025/12/11

Abstract

Generative networks are an exciting tool for fast LHC event 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.

Cited by

• Neural Control Variates at LO and NLO
• Economical Jet Taggers -- Equivariant, Slim, and Quantized
• Forecasting Generative Amplification
• Lorentz-Equivariance without Limitations

Discussions

• Can transformers learn the universal patterns of jet radiation and extrapolate beyond the training data? Find out in our preprint 'Extrapolating Jet Radiation with Autoregressive Transformers' arxiv [bsky, 4 points, 1 comments]

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