SciPost Submission Page
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
This Submission thread is now published as
Submission summary
| Authors (as registered SciPost users): | Tony Menzo |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2311.09296v2 (pdf) |
| Code repository: | https://gitlab.com/uchep/mlhad |
| 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 |
| Specialties: |
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| Approaches: | Theoretical, Experimental, Computational, Phenomenological |
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 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
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.
Published as SciPost Phys. 17, 045 (2024)