Towards a data-driven model of hadronization using normalizing flows
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.
- Publication:
-
SciPost Physics
- Pub Date:
- August 2024
- DOI:
- 10.21468/SciPostPhys.17.2.045
- arXiv:
- arXiv:2311.09296
- Bibcode:
- 2024ScPP...17...45B
- Keywords:
-
- High Energy Physics - Phenomenology;
- High Energy Physics - Experiment
- E-Print:
- 26 pages, 9 figures, public code available