Contribution for PyHEP 2022: End-to-end physics analysis with Open Data: the Analysis Grand Challenge (Alexander Held, Oksana Shadura).

This is heavily based on material from the main Analysis Grand Challenge repository.

Click the "launch binder" badge above.

The IRIS-HEP Analysis Grand Challenge repository includes links to more material.

The IRIS-HEP Analysis Grand Challenge (AGC) provides an environment for investigating analysis methods in the context of a realistic physics analysis. It features an analysis task that captures all relevant workflow aspects encountered in LHC analyses, reaching from data delivery to statistical inference. By using publicly available Open Data, the AGC allows anyone interested to test different analysis approaches and implementations at scale.

This tutorial showcases a complete Python implementation of the AGC analysis task, making heavy use of Scikit-HEP libraries and coffea. It demonstrates how these libraries provide the required functionality and interfaces for an end-to-end analysis pipeline. This includes the organization of input datasets, columnar data processing, evaluation of systematic uncertainties, histogram creation, statistical model assembly and inference, alongside the relevant visualizations that a physicist running this pipeline requires.

This work was supported by the U.S. National Science Foundation (NSF) cooperative agreement OAC-1836650 (IRIS-HEP).