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
Submission summary
| Authors (as registered SciPost users): | Christian Bierlich · Tony Menzo · Stephen Mrenna · Manuel Szewc |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2311.09296v2 (pdf) |
| Code repository: | https://gitlab.com/uchep/mlhad |
| Date accepted: | June 19, 2024 |
| Date submitted: | April 23, 2024, 6:10 p.m. |
| Submitted by: | Tony Menzo |
| 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)