A study of CMS analysis pipelines through the Integration Challenge

Abstract

The upcoming High-Luminosity Large Hadron Collider (HL-LHC) at CERN will deliver an unprecedented volume of data for High Energy Physics (HEP). This wealth of information offers significant opportunities for scientific discovery, but its scale challenges traditional analysis workflows. In this paper, we present CMS analysis pipelines being developed to meet HL-LHC demands. These pipelines build on the broader scientific Python ecosystem, complemented by solutions specifically designed for HEP. A central focus of this work is the Integration Challenge, an IRIS-HEP led effort aimed at assessing the readiness of developed software stack to be used in real world physics analysis and improving the readiness of analysis facilities for the HL-LHC era. The Integration Challenge acts as an end-to-end integration test: by implementing a complete physics analysis pipeline, it evaluates tool interoperability and the overall user experience for analysts. The current pipeline includes columnar data processing, machine learning, statistical inference, and visualization tasks covering a variety of CMS analysis scenarios. In addition, the Integration Challenge explores efficient strategies for delivering skimmed data using diverse tools and data formats, as well as evaluating the ServiceX data-delivery system for HEP analyses. Throughout the testing phase, we also investigated several prototype services, such as histogram-as-a-service capabilities, along with other emerging services that may support future HL-LHC analysis workflows.