Machine Learning for Columnar High Energy Physics Analysis Article Swipe
Elliott Kauffman
,
A. Held
,
Oksana Shadura
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.01802
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.01802
Machine learning (ML) has become an integral component of high energy physics data analyses and is likely to continue to grow in prevalence. Physicists are incorporating ML into many aspects of analysis, from using boosted decision trees to classify particle jets to using unsupervised learning to search for physics beyond the Standard Model. Since ML methods have become so widespread in analysis and these analyses need to be scaled up for HL-LHC data, neatly integrating ML training and inference into scalable analysis workflows will improve the user experience of analysis in the HL-LHC era. We present the integration of ML training and inference into the IRIS-HEP Analysis Grand Challenge (AGC) pipeline to provide an example of how this integration can look like in a realistic analysis environment. We also utilize Open Data to ensure the project's reach to the broader community. Different approaches for performing ML inference at analysis facilities are investigated and compared, including performing inference through external servers. Since ML techniques are applied for many different types of tasks in physics analyses, we showcase options for ML integration that can be applied to various inference needs.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.01802
- https://arxiv.org/pdf/2401.01802
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390602451
All OpenAlex metadata
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https://openalex.org/W4390602451Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.01802Digital Object Identifier
- Title
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Machine Learning for Columnar High Energy Physics AnalysisWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-01-03Full publication date if available
- Authors
-
Elliott Kauffman, A. Held, Oksana ShaduraList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.01802Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.01802Direct 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
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https://arxiv.org/pdf/2401.01802Direct OA link when available
- Concepts
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Inference, Large Hadron Collider, Workflow, Pipeline (software), Machine learning, Computer science, Artificial intelligence, Scalability, Data science, Unsupervised learning, Particle physics, Physics, Database, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.beyond | 49 |
| abstract_inverted_index.energy | 10 |
| abstract_inverted_index.ensure | 133 |
| abstract_inverted_index.likely | 16 |
| abstract_inverted_index.neatly | 73 |
| abstract_inverted_index.needs. | 187 |
| abstract_inverted_index.scaled | 68 |
| abstract_inverted_index.search | 46 |
| abstract_inverted_index.Machine | 0 |
| abstract_inverted_index.applied | 164, 183 |
| abstract_inverted_index.aspects | 29 |
| abstract_inverted_index.boosted | 34 |
| abstract_inverted_index.broader | 139 |
| abstract_inverted_index.example | 114 |
| abstract_inverted_index.improve | 84 |
| abstract_inverted_index.methods | 55 |
| abstract_inverted_index.options | 176 |
| abstract_inverted_index.physics | 11, 48, 172 |
| abstract_inverted_index.present | 95 |
| abstract_inverted_index.provide | 112 |
| abstract_inverted_index.through | 157 |
| abstract_inverted_index.utilize | 129 |
| abstract_inverted_index.various | 185 |
| abstract_inverted_index.Analysis | 106 |
| abstract_inverted_index.IRIS-HEP | 105 |
| abstract_inverted_index.Standard | 51 |
| abstract_inverted_index.analyses | 13, 64 |
| abstract_inverted_index.analysis | 61, 81, 89, 125, 148 |
| abstract_inverted_index.classify | 38 |
| abstract_inverted_index.continue | 18 |
| abstract_inverted_index.decision | 35 |
| abstract_inverted_index.external | 158 |
| abstract_inverted_index.integral | 6 |
| abstract_inverted_index.learning | 1, 44 |
| abstract_inverted_index.particle | 39 |
| abstract_inverted_index.pipeline | 110 |
| abstract_inverted_index.scalable | 80 |
| abstract_inverted_index.servers. | 159 |
| abstract_inverted_index.showcase | 175 |
| abstract_inverted_index.training | 76, 100 |
| abstract_inverted_index.Challenge | 108 |
| abstract_inverted_index.Different | 141 |
| abstract_inverted_index.analyses, | 173 |
| abstract_inverted_index.analysis, | 31 |
| abstract_inverted_index.compared, | 153 |
| abstract_inverted_index.component | 7 |
| abstract_inverted_index.different | 167 |
| abstract_inverted_index.including | 154 |
| abstract_inverted_index.inference | 78, 102, 146, 156, 186 |
| abstract_inverted_index.project's | 135 |
| abstract_inverted_index.realistic | 124 |
| abstract_inverted_index.workflows | 82 |
| abstract_inverted_index.Physicists | 23 |
| abstract_inverted_index.approaches | 142 |
| abstract_inverted_index.community. | 140 |
| abstract_inverted_index.experience | 87 |
| abstract_inverted_index.facilities | 149 |
| abstract_inverted_index.performing | 144, 155 |
| abstract_inverted_index.techniques | 162 |
| abstract_inverted_index.widespread | 59 |
| abstract_inverted_index.integrating | 74 |
| abstract_inverted_index.integration | 97, 118, 179 |
| abstract_inverted_index.prevalence. | 22 |
| abstract_inverted_index.environment. | 126 |
| abstract_inverted_index.investigated | 151 |
| abstract_inverted_index.unsupervised | 43 |
| abstract_inverted_index.incorporating | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile |