The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
Nathan Allen Grieser1, Eduardo Rodrigues2, Niladri Sahoo3
1University of Cincinnati, Cincinnati, USA.
Computing and Software for Big Science
|December 1, 2025
Summary
The LHCb Stripping project refines collision data for analysis, optimizing processing for legacy and live datasets. This ensures continued access to valuable physics data for the LHCb collaboration.
Area of Science:
- High Energy Physics
- Particle Physics Data Analysis
Background:
- The LHCb experiment generates vast amounts of collision data requiring sophisticated processing.
- Efficient data refinement is crucial for offline analysis and scientific discovery.
Purpose of the Study:
- To provide a comprehensive overview of the LHCb Stripping project's framework and operations.
- To detail advancements in data processing infrastructure and campaign management.
- To discuss challenges and future directions for legacy and live data handling.
Main Methods:
- Utilizing a Python-configurable architecture for the Stripping framework.
- Integrating the framework with LHCb computing systems for large-scale campaign management.
- Implementing GitLab-based workflows, continuous integration, and automation for efficient processing.
Main Results:
- Successful re-analysis of Runs 1 and 2 legacy data.
- Optimization of infrastructure for both legacy and live data processing.
- Advancements in organizational and computational processing techniques.
Conclusions:
- The Stripping project is vital for managing LHCb data, ensuring access to valuable physics datasets.
- Continuous optimization and adoption of modern workflows are key to efficient data processing.
- Lessons learned will guide future roadmaps for sustained data access and analysis.
