A simplified configuration for common algorithms for ATLAS analysis

In the ATLAS analysis model, users interact with specialized algorithms to perform a variety of tasks on their physics objects including calibration, identification, and obtaining systematic uncertainties for simulated events. These algorithms have a wide variety of configurations, and often must be applied in specific orders. A user-friendly configuration mechanism has been developed with the goal of improving the user experience from the perspective of both ease-of-use and stability. Users can now configure necessary algorithms via a YAML file, enabled by a physics-oriented python configuration. The configuration mechanism and training are discussed.§

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701114
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

A simplified configuration for common algorithms for ATLAS analysis

H. L. Russell, Verena Martinez Outschoorn, Matthew Kenneth Maroun, N. Krumnack et al.
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

A simplified configuration for common algorithms for ATLAS analysis

H. L. Russell, Verena Martinez Outschoorn, Matthew Kenneth Maroun, N. Krumnack, Joseph Earl Lambert, W. A. Leight
article en

Abstract

In the ATLAS analysis model, users interact with specialized algorithms to perform a variety of tasks on their physics objects including calibration, identification, and obtaining systematic uncertainties for simulated events. These algorithms have a wide variety of configurations, and often must be applied in specific orders. A user-friendly configuration mechanism has been developed with the goal of improving the user experience from the perspective of both ease-of-use and stability. Users can now configure necessary algorithms via a YAML file, enabled by a physics-oriented python configuration. The configuration mechanism and training are discussed.§

EPJ Web of ConferencesVol. 337
Iowa State University (US), University of Victoria (CA), University of Massachusetts Amherst (US)
Openalex Percentile: Top 68%
Distributed and Parallel Computing Systems
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