Parton labeling without matching: unveiling emergent labelling capabilities in regression models Article Swipe
Shikai Qiu
,
S. Han
,
X. Ju
,
Benjamin Nachman
,
Haichen Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1140/epjc/s10052-023-11809-z
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1140/epjc/s10052-023-11809-z
Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely-used $$\chi ^2$$ method.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1140/epjc/s10052-023-11809-z
- https://link.springer.com/content/pdf/10.1140/epjc/s10052-023-11809-z.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384405708
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384405708Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1140/epjc/s10052-023-11809-zDigital Object Identifier
- Title
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Parton labeling without matching: unveiling emergent labelling capabilities in regression modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-15Full publication date if available
- Authors
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Shikai Qiu, S. Han, X. Ju, Benjamin Nachman, Haichen WangList of authors in order
- Landing page
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https://doi.org/10.1140/epjc/s10052-023-11809-zPublisher landing page
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https://link.springer.com/content/pdf/10.1140/epjc/s10052-023-11809-z.pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1140/epjc/s10052-023-11809-z.pdfDirect OA link when available
- Concepts
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Parton, Matching (statistics), Particle physics, Regression, Quark, Collider, Machine learning, Computer science, Artificial intelligence, Physics, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 2, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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