Extrapolating Jet Radiation with Autoregressive Transformers

Fuente: arXiv
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Main Authors: Butter, Anja, Charton, François, Villadamigo, Javier Mariño, Ore, Ayodele, Plehn, Tilman, Spinner, Jonas
Format: Preprint
Published: 2024
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author Butter, Anja
Charton, François
Villadamigo, Javier Mariño
Ore, Ayodele
Plehn, Tilman
Spinner, Jonas
author_facet Butter, Anja
Charton, François
Villadamigo, Javier Mariño
Ore, Ayodele
Plehn, Tilman
Spinner, Jonas
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2412_12074
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Extrapolating Jet Radiation with Autoregressive Transformers
Butter, Anja
Charton, François
Villadamigo, Javier Mariño
Ore, Ayodele
Plehn, Tilman
Spinner, Jonas
High Energy Physics - Phenomenology
Machine Learning
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.
title Extrapolating Jet Radiation with Autoregressive Transformers
topic High Energy Physics - Phenomenology
Machine Learning
url https://arxiv.org/abs/2412.12074