Integrating Particle Flavor into Deep Learning Models for Hadronization Article Swipe
J. Chan
,
X. Ju
,
Adam Kania
,
Benjamin Nachman
,
Vishnu Sangli
,
Andrzej Siódmok
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.08453
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.08453
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 parameterizations 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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.08453
- https://arxiv.org/pdf/2312.08453
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389814178
All OpenAlex metadata
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https://openalex.org/W4389814178Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.08453Digital Object Identifier
- Title
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Integrating Particle Flavor into Deep Learning Models for HadronizationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-13Full publication date if available
- Authors
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J. Chan, X. Ju, Adam Kania, Benjamin Nachman, Vishnu Sangli, Andrzej SiódmokList of authors in order
- Landing page
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https://arxiv.org/abs/2312.08453Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2312.08453Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2312.08453Direct OA link when available
- Concepts
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Hadronization, Particle physics, Context (archaeology), Event (particle physics), Kinematics, Physics, Computer science, Artificial intelligence, Hadron, Biology, Quantum mechanics, Paleontology, Classical mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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