Realistic environments for prototyping, studying and improving analysis workflows are a crucial element on the way towards user-friendly physics analysis at HL-LHC scale. The IRIS-HEP Analysis Grand Challenge (AGC) provides such an environment. It defines a scalable and modular analysis task that captures relevant workflow aspects, ranging from large-scale data processing and handling of systematic uncertainties to statistical inference and analysis preservation. By being based on publicly available Open Data, the AGC provides a point of contact for the broader community. Multiple different implementations of the analysis task that make use of various pipelines and software stacks already exist.
This contribution presents an updated AGC analysis task. It features a machine learning component and expanded analysis complexity, including the handling of an extended and more realistic set of systematic uncertainties. These changes both align the AGC further with analysis needs at the HL-LHC and allow for probing an increased set of functionality.
Another focus is the showcase of a reference AGC implementation, which is heavily based on the HEP Python ecosystem and uses modern analysis facilities. The integration of various data delivery strategies is described, resulting in multiple analysis pipelines that are compared to each other.
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