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.
Cite
@article{arxiv.2311.09296,
title = {Towards a data-driven model of hadronization using normalizing flows},
author = {Christian Bierlich and Phil Ilten and Tony Menzo and Stephen Mrenna and Manuel Szewc and Michael K. Wilkinson and Ahmed Youssef and Jure Zupan},
journal= {arXiv preprint arXiv:2311.09296},
year = {2024}
}
Comments
26 pages, 9 figures, public code available