| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01359 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701359 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701359
Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem
1 University of Texas at Austin, Austin, Texas, USA
2 University of Wisconsin-Madison, Madison, Wisconsin, USA
3 Ludwig Maximilians Universitat, Munich, Germany
4 Technical University of Munich, Munich, Germany
5 Santa Cruz Institute for Particle Physics, Santa Cruz, California, USA
6 University of Washington, Seattle, Washington, USA
* Corresponding author e-mail: matthew.feickert@cern.ch
Published online: 7 October 2025
The ATLAS experiment is in the process of developing a columnar analysis demonstrator, which takes advantage of the Python ecosystem of data science tools. This project is inspired by the analysis demonstrator from IRIS-HEP. The demonstrator employs PHYSLITE OpenData from the ATLAS collaboration, the new Run 3 compact ATLAS analysis data format. The tight integration of ROOT features within PHYSLITE presents unique challenges when integrating with the Python analysis ecosystem. The demonstrator is constructed from ATLAS PHYSLITE OpenData, ensuring the accessibility and reproducibility of the analysis. The analysis pipeline of the demonstrator incorporates a comprehensive suite of tools and libraries. These include uproot for data reading, awkward-array for data manipulation, Dask for parallel computing, and hist for histogram processing. For the purpose of statistical analysis, the pipeline integrates cabinetry and pyhf, providing a robust toolkit for analysis. A significant component of this project is the custom application of corrections, scale factors, and systematic errors using ATLAS software. The infrastructure and methodology for these applications will be discussed in detail during the presentation, underscoring the adaptability of the Python ecosystem for high energy physics analysis.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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