Sequence-based Machine Learning Models in Jet Physics Article Swipe
R. Teixeira De Lima
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.06128
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2102.06128
Sequence-based modeling broadly refers to algorithms that act on data that is represented as an ordered set of input elements. In particular, Machine Learning algorithms with sequences as inputs have seen successfull applications to important problems, such as Natural Language Processing (NLP) and speech signal modeling. The usage this class of models in collider physics leverages their ability to act on data with variable sequence lengths, such as constituents inside a jet. In this document, we explore the application of Recurrent Neural Networks (RNNs) and other sequence-based neural network architectures to classify jets, regress jet-related quantities and to build a physics-inspired jet representation, in connection to jet clustering algorithms. In addition, alternatives to sequential data representations are briefly discussed.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2102.06128
- https://arxiv.org/pdf/2102.06128
- OA Status
- green
- Cited By
- 2
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3129104960
All OpenAlex metadata
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https://openalex.org/W3129104960Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2102.06128Digital Object Identifier
- Title
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Sequence-based Machine Learning Models in Jet PhysicsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-02-09Full publication date if available
- Authors
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R. Teixeira De LimaList of authors in order
- Landing page
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https://arxiv.org/abs/2102.06128Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2102.06128Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2102.06128Direct OA link when available
- Concepts
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Jet (fluid), Sequence (biology), Cluster analysis, Artificial neural network, Representation (politics), Artificial intelligence, Set (abstract data type), Collider, Computer science, Algorithm, Physics, Particle physics, Programming language, Politics, Biology, Law, Thermodynamics, Genetics, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2023: 2Per-year citation counts (last 5 years)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.application | 78 |
| abstract_inverted_index.jet-related | 94 |
| abstract_inverted_index.particular, | 21 |
| abstract_inverted_index.represented | 12 |
| abstract_inverted_index.successfull | 31 |
| abstract_inverted_index.alternatives | 111 |
| abstract_inverted_index.applications | 32 |
| abstract_inverted_index.constituents | 68 |
| abstract_inverted_index.architectures | 89 |
| abstract_inverted_index.Sequence-based | 0 |
| abstract_inverted_index.sequence-based | 86 |
| abstract_inverted_index.representation, | 102 |
| abstract_inverted_index.representations | 115 |
| abstract_inverted_index.physics-inspired | 100 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5114375423 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I97018004 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7699999809265137 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |