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.
@article{arxiv.2509.05294,
title = {The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics},
author = {Nathan Grieser and Eduardo Rodrigues and Niladri Sahoo and Shuqi Sheng and Nicole Skidmore and Mark Smith},
journal= {arXiv preprint arXiv:2509.05294},
year = {2025}
}