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Towards a data-driven model of hadronization using normalizing flows

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arxiv 2311.09296 v2 pith:KGMGNFQV submitted 2023-11-15 hep-ph hep-ex

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 This is my paper
classification hep-ph hep-ex
keywords hadronizationmodeldistributionsexperimentalflowsintroducenormalizingtraining
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 3 Pith papers

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  2. Unbinned extraction of $\gamma$ from $B\to DK$ with normalizing flows

    hep-ph 2026-05 unverdicted novelty 7.0

    Normalizing flows trained on D decay data create a continuous unbinned model of Dalitz plot amplitudes, allowing extraction of gamma from B decay data with successful recovery of injected values in Monte Carlo tests.

  3. Data-Driven Predictions for Dark Photon and Millicharged Particle Production

    hep-ph 2025-12 unverdicted novelty 7.0

    A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.