Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem

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.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-07
DOI
https://doi.org/10.1051/epjconf/202533701359/pdf
Primary Topic
Computational Physics and Python Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem

Alexander Held, Matthias Vigl, Kyungeon Choi, Nils Krumnack et al.
Springer Link (Chiba Institute of Technology)
Computational Physics and Python Applications
article

Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem

Alexander Held, Matthias Vigl, Kyungeon Choi, Nils Krumnack, Gordon Watts, Lukas Alexander Heinrich, Matthew Feickert, Evangelos Kourlitis, Giordon Stark, Nikolai Hartmann
article en

Abstract

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.

Springer Link (Chiba Institute of Technology)
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
Computational Physics and Python Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Building a Columnar Analysis Demonstrator for ATLAS PHYSLITE Open Data using the Python Ecosystem — Alexander Held, Matthias Vigl, et al. · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS