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Towards a data-driven model of hadronization using normalizing flows

by Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan

Submission summary

Authors (as registered SciPost users): Tony Menzo
Submission information
Preprint Link:  (pdf)
Code repository:
Date accepted: 2024-06-19
Date submitted: 2024-04-23 18:10
Submitted by: Menzo, Tony
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Phenomenology
Approaches: Theoretical, Experimental, Computational, Phenomenological


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.

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

Author comments upon resubmission

We thank the reviewers for the detailed reading of our manuscript. We have updated our manuscript with a number of changes based on their suggestions.

List of changes

- Included additional details and clarifying remarks on comparisons to cited work within the introduction, as suggested by the referees.
- Added a footnote on page 3 providing a qualitative remark on the speed of the presented hadronization model compared to modern event generators.
- Added a quantitative remark on the speed of re-weighting versus re-simulating during fine-tuning in section 4.
- Added an expanded discussion regarding multi-dimensional MAGIC fine-tuning in the second to last paragraph of section 4.
- Included additional references and an extended discussion in section 5 on Bayesian error estimation and its ability to quantify uncertainties related to the under- and over-fitting of training data.
- Provided additional details on the binning used in the right panel of figure 4 within the figure caption, as suggested by the referees.

Current status:
Accepted in target Journal

Editorial decision: For Journal SciPost Physics: Publish
(status: Editorial decision fixed and (if required) accepted by authors)

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