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arXiv · 2311.09296

Towards a data-driven model of hadronization using normalizing flows

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

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Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan. 2024-04-19. Towards a data-driven model of hadronization using normalizing flows. https://doi.org/10.21468/scipostphys.17.2.045

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