Abstract
The offline software framework of the LHCb experiment has undergone a significant overhaul to tackle the data processing challenges that will arise in the upcoming Run 3 and Run 4 of the Large Hadron Collider. This paper introduces FunTuple, a novel component developed for offline data processing within the LHCb experiment. This component enables the computation and storage of a diverse range of observables for both reconstructed and simulated events by leveraging on the tools initially developed for the trigger system. This feature is crucial for ensuring consistency between trigger-computed and offline-analysed observables. The component and its tool suite offer users flexibility to customise stored observables, and its reliability is validated through a full-coverage set of rigorous unit tests. This paper comprehensively explores FunTuple’s design, interface, interaction with other algorithms, and its role in facilitating offline data processing for the LHCb experiment for the next decade and beyond.
| Original language | English |
|---|---|
| Article number | 6 |
| Pages (from-to) | 1-11 |
| Number of pages | 11 |
| Journal | Computing and Software for Big Science |
| Volume | 8 |
| Early online date | 24 Feb 2024 |
| DOIs | |
| Publication status | Published - 2024 |
Bibliographical note
Publisher Copyright:© The Author(s) 2024.
Funding
We extend our sincere appreciation to our collaborators in the Data Processing and Analysis (DPA) project for their insightful discussions, input, and unwavering support throughout the work. We are particularly grateful to Maurizio Martinelli for his work in documenting examples pertaining to the FunTuple, and to Davide Fazzini for his contribution in developing diverse unit tests for the component. We acknowledge Sascha Stahl for his tests aimed at optimising the components’s speed. Our appreciation also extends to the members of the Real Time Analysis (RTA) project for their feedback and suggestions on ThOr functor usage. Additionally, we extend a special thank you to Christoph Hasse for his contributions to the development of the composition mechanism, which has enhanced the flexibility of using ThOr functors for offline processing. We also convey our gratitude to the members of the Early Measurement Task Force (EMTF) for Run 3 for their rigorous stress-testing, invaluable feedback, and ongoing work in expanding the FunctorCollection library within the DaVinci framework. This work received essential support from the Forschungskredit of the University of Zurich under grant number FK-21-129 and the Swiss National Science Foundation under contract 204238. We extend our sincere appreciation to our collaborators in the Data Processing and Analysis (DPA) project for their insightful discussions, input, and unwavering support throughout the work. We are particularly grateful to Maurizio Martinelli for his work in documenting examples pertaining to the FunTuple, and to Davide Fazzini for his contribution in developing diverse unit tests for the component. We acknowledge Sascha Stahl for his tests aimed at optimising the components’s speed. Our appreciation also extends to the members of the Real Time Analysis (RTA) project for their feedback and suggestions on ThOr functor usage. Additionally, we extend a special thank you to Christoph Hasse for his contributions to the development of the composition mechanism, which has enhanced the flexibility of using ThOr functors for offline processing. We also convey our gratitude to the members of the Early Measurement Task Force (EMTF) for Run 3 for their rigorous stress-testing, invaluable feedback, and ongoing work in expanding the FunctorCollection library within the DaVinci framework. This work received essential support from the Forschungskredit of the University of Zurich under grant number FK-21-129 and the Swiss National Science Foundation under contract 204238.
| Funders | Funder number |
|---|---|
| Early Measurement Task Force | |
| Sascha Stahl | |
| Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung | 204238 |
| Universität Zürich | FK-21-129 |
Keywords
- Data processing and offline analysis
- High-energy-physics
- LHCb experiment