mlsanityData to 14 Oct 2026

Extrapolating Jet Radiation with Autoregressive Transformers

arXiv 2412.12074 ↗First posted 16 Dec 2024hep-ph cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 40 of its 115 references that are in the map. First posted on arXiv 16 Dec 2024.

Builds on

40 of 115 references.

References · 40

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28 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Scaling laws for amplitude surrogatescited by 6
  2. 2025Forecasting Generative Amplificationcited by 3