mlsanityData to 14 Oct 2026

FAIR AI Models in High Energy Physics

arXiv 2212.05081 ↗First posted 9 Dec 2022hep-ex cs.LG physics.comp-phcited by 4

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Abstract (arXiv)

The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning (ML) models -- algorithms that have been trained on data without being explicitly programmed -- and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template's use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 31 of its 114 references that are in the map. First posted on arXiv 9 Dec 2022.

Builds on

31 of 114 references.

References · 31

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19 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  2. 2023Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorscited by 4
  3. 2026Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physicscited by 2
  4. 2025Enhancing Transparency and Traceability in Healthcare AI: The AI Product Passportcited by 0