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Extrapolating Jet Radiation with Autoregressive Transformers

by Anja Butter, François Charton, Javier Mariño Villadamigo, Ayodele Ore, Tilman Plehn, Jonas Spinner

Submission summary

Authors (as registered SciPost users): Javier Mariño Villadamigo · Ayodele Ore · Tilman Plehn · Jonas Spinner
Submission information
Preprint Link: https://arxiv.org/abs/2412.12074v1  (pdf)
Code repository: https://github.com/heidelberg-hepml/jetgpt-splittings
Date submitted: 2024-12-31 11:47
Submitted by: Spinner, Jonas
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological

Abstract

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.

Author indications on fulfilling journal expectations

  • Provide a novel and synergetic link between different research areas.
  • Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
  • Detail a groundbreaking theoretical/experimental/computational discovery
  • Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing

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