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Towards a data-driven model of hadronization using normalizing flows

Bierlich, Christian LU ; Ilten, Phil ; Menzo, Tony ; Mrenna, Stephen ; Szewc, Manuel ; Wilkinson, Michael K. ; Youssef, Ahmed and Zupan, Jure (2024) In SciPost Physics 17(2).
Abstract

We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on... (More)

We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
SciPost Physics
volume
17
issue
2
article number
045
publisher
SciPost
external identifiers
  • scopus:85201077333
ISSN
2542-4653
DOI
10.21468/SciPostPhys.17.2.045
language
English
LU publication?
yes
id
695e61d8-51c5-4db0-a1ca-36685883fcc0
date added to LUP
2024-09-06 14:19:11
date last changed
2025-04-04 14:00:51
@article{695e61d8-51c5-4db0-a1ca-36685883fcc0,
  abstract     = {{<p>We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.</p>}},
  author       = {{Bierlich, Christian and Ilten, Phil and Menzo, Tony and Mrenna, Stephen and Szewc, Manuel and Wilkinson, Michael K. and Youssef, Ahmed and Zupan, Jure}},
  issn         = {{2542-4653}},
  language     = {{eng}},
  number       = {{2}},
  publisher    = {{SciPost}},
  series       = {{SciPost Physics}},
  title        = {{Towards a data-driven model of hadronization using normalizing flows}},
  url          = {{http://dx.doi.org/10.21468/SciPostPhys.17.2.045}},
  doi          = {{10.21468/SciPostPhys.17.2.045}},
  volume       = {{17}},
  year         = {{2024}},
}