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DOE OSTI · 2429011

Towards a data-driven model of hadronization using normalizing flows

Bierlich, Christian (ORCID:0000000239786085)·Ilten, Philip (ORCID:0000000155341732)·Menzo, Tony (ORCID:0000000220134570)·Mrenna, Stephen·Szewc, Manuel·Wilkinson, Michael K.·Youssef, Ahmed (ORCID:0000000315854757)·Zupan, Jure (ORCID:000000019445537X)
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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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Bierlich, Christian (ORCID:0000000239786085), Ilten, Philip (ORCID:0000000155341732), Menzo, Tony (ORCID:0000000220134570), Mrenna, Stephen, Szewc, Manuel, Wilkinson, Michael K., Youssef, Ahmed (ORCID:0000000315854757), Zupan, Jure (ORCID:000000019445537X). 2024-08-12. Towards a data-driven model of hadronization using normalizing flows. https://doi.org/10.21468/scipostphys.17.2.045

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