Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
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|
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Article Number | 08008 | |
Number of page(s) | 8 | |
Section | Collaboration, Reinterpretation, Outreach and Education | |
DOI | https://doi.org/10.1051/epjconf/202429508008 | |
Published online | 06 May 2024 |
https://doi.org/10.1051/epjconf/202429508008
Facilitating the preservation of LHCb Analyses with APD
1 CERN
2 The University of Edinburgh
* e-mail: chris.burr@cern.ch
** e-mail: ben.couturier@cern.ch
*** e-mail: r.oneil@cern.ch
Published online: 6 May 2024
High Energy Physics experiments at the Large Hadron Collider generate petabytes of data per year that go through multiple transformations before final analysis and paper publication. Recording the provenance of these data is therefore crucial to maintain the quality of the final results. While tools are in place within LHCb to keep this information for the common experiment-wide transforms, analysts have had to implement their own solutions for the steps dealing with ntuples. This gap between centralised and interactive processing can become problematic. In order to facilitate the task, ntuples extracted by LHCb analysts via so-called “Analysis Productions” are tracked in the experiment bookkeeping database and can be enriched with extra information about their meaning and intended use. This information can then be used to access ntuples more easily: a set of Python tools allow querying of ntuple file locations with associated metadata, and integrate their processing within analysis workflows. The tools are designed with the intention of ensuring analysis code continues to be functional into the future and are robust against evolutions in how data is accessed. This paper presents the integration of these new tools into the LHCb codebase and demonstrates how they will be used in LHCb data processing and analysis.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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