SciPost Submission Page
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 | |
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| 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: |
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| 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