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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.05294 |
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| _version_ | 1866917152929349632 |
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| author | Grieser, Nathan Rodrigues, Eduardo Sahoo, Niladri Sheng, Shuqi Skidmore, Nicole Smith, Mark |
| author_facet | Grieser, Nathan Rodrigues, Eduardo Sahoo, Niladri Sheng, Shuqi Skidmore, Nicole Smith, Mark |
| contents | The LHCb Stripping project is a pivotal component of the experiment's data processing framework, designed to refine vast volumes of collision data into manageable samples for offline analysis. It ensures the re-analysis of Runs 1 and 2 legacy data, maintains the software stack, and executes (re-)Stripping campaigns. As the focus shifts toward newer data sets, the project continues to optimize infrastructure for both legacy and live data processing. This paper provides a comprehensive overview of the Stripping framework, detailing its Python-configurable architecture, integration with LHCb computing systems, and large-scale campaign management. We highlight organizational advancements such as GitLab-based workflows, continuous integration, automation, and parallelized processing, alongside computational challenges. Finally, we discuss lessons learned and outline a future road-map to sustain efficient access to valuable physics legacy data sets for the LHCb collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05294 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics Grieser, Nathan Rodrigues, Eduardo Sahoo, Niladri Sheng, Shuqi Skidmore, Nicole Smith, Mark High Energy Physics - Experiment The LHCb Stripping project is a pivotal component of the experiment's data processing framework, designed to refine vast volumes of collision data into manageable samples for offline analysis. It ensures the re-analysis of Runs 1 and 2 legacy data, maintains the software stack, and executes (re-)Stripping campaigns. As the focus shifts toward newer data sets, the project continues to optimize infrastructure for both legacy and live data processing. This paper provides a comprehensive overview of the Stripping framework, detailing its Python-configurable architecture, integration with LHCb computing systems, and large-scale campaign management. We highlight organizational advancements such as GitLab-based workflows, continuous integration, automation, and parallelized processing, alongside computational challenges. Finally, we discuss lessons learned and outline a future road-map to sustain efficient access to valuable physics legacy data sets for the LHCb collaboration. |
| title | The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2509.05294 |