Performing a physics analysis of data from simulations of a high energy experiment requires the application of several common procedures, from obtaining and reading the data to producing detailed plots for interpretation. Implementing common procedures in a general analysis framework allows the analyzer to focus on the unique parts of their analysis. Over the past few years, EIC simulations have been performed using differing frameworks and data models; we thus developed
epic-analysis, a common analysis framework to support all of them, allowing for comparison studies and cross checks while the design of the EIC continues to evolve. The reconstruction of kinematic variables is fundamental to several physics channels, including inclusive, semi-inclusive, and jet physics.
epic-analysis includes many different kinematics reconstruction methods, ranging from using the scattered electron to machine learning methods, each of which produce the same set of kinematic variables needed for physics analysis. Since the number of variables is large, a multi-dimensionally binned analysis is also often employed. We thus developed
adage, a novel graph-based data structure that not only associates data to their bins, but also stores and can execute user-specified algorithms on any lower dimensional subsets. This approach allows the analyzer to write analysis algorithms that are fully independent of the binning strategy, expediting the exploration of the high dimensional phase space. Finally, as part of the EPIC software stack,
epic-analysis continuous integration tests can be triggered by upstream changes in the simulation or reconstruction. For example, this automation allows for the physics impact on a detector design change to be quickly assessed, completing the full feedback loop for EIC detector design.
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