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Integrating particle flavor into deep learning models for hadronization

Jay Chan1, Xiangyang Ju1, Adam Kania2, Benjamin Nachman3,4, Vishnu Sangli5,3, and Andrzej Siodmok2

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Phys. Rev. D 111, 116015 – Published 17 June, 2025

DOI: https://doi.org/10.1103/hgbg-k7js

Abstract

Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there have been multiple proposals to improve the accuracy of hadronization models by utilizing more flexible parametrizations based on neural networks. These recent proposals have focused on the kinematic properties of hadrons, but a full model must also include particle flavor. In this paper, we show how to build a deep learning-based hadronization model that includes both kinematic (continuous) and flavor (discrete) degrees of freedom. Our approach is based on generative adversarial networks and we show the performance within the context of the cluster hadronization model within the herwig event generator.

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Phys. Rev. D 111, 116015– Published 17 June, 2025
  • Received 26 January 2025
  • Accepted 19 May 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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