Modeling hadronization using machine learning Article Swipe

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P. Ilten , Tony Menzo , Ahmed Youssef , Jure Zupan ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.21468/scipostphys.14.3.027

We present the first steps in the development of a new class of hadronization models utilizing machine learning techniques. We successfully implement, validate, and train a conditional sliced-Wasserstein autoencoder to replicate the Pythia generated kinematic distributions of first-hadron emissions, when the Lund string model of hadronization implemented in Pythia is restricted to the emissions of pions only. The trained models are then used to generate the full hadronization chains, with an IR cutoff energy imposed externally. The hadron multiplicities and cumulative kinematic distributions are shown to match the Pythia generated ones. We also discuss possible future generalizations of our results.

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Hadronization Kinematics Pion Computer science Particle physics Hadron Physics Statistical physics Classical mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.21468/scipostphys.14.3.027
PDF: https://scipost.org/10.21468/SciPostPhys.14.3.027/pdf
OA Status: diamond
Cited By: 27
References: 87
Related Works: 10
OpenAlex ID: https://openalex.org/W4322766541

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DOI: https://doi.org/10.21468/scipostphys.14.3.027

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Title: Modeling hadronization using machine learning

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-03-02

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Authors: P. Ilten, Tony Menzo, Ahmed Youssef, Jure Zupan

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Landing page: https://doi.org/10.21468/scipostphys.14.3.027

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OA URL: https://scipost.org/10.21468/SciPostPhys.14.3.027/pdf

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Concepts: Hadronization, Kinematics, Pion, Computer science, Particle physics, Hadron, Physics, Statistical physics, Classical mechanics

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Cited by: 27

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