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Towards a data-driven model of hadronization using normalizing flows
- Source :
- SciPost Phys. 17, 045 (2024)
- Publication Year :
- 2023
-
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.<br />Comment: 26 pages, 9 figures, public code available
- Subjects :
- High Energy Physics - Phenomenology
High Energy Physics - Experiment
Subjects
Details
- Database :
- arXiv
- Journal :
- SciPost Phys. 17, 045 (2024)
- Publication Type :
- Report
- Accession number :
- edsarx.2311.09296
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.21468/SciPostPhys.17.2.045