FAIR AI models in high energy physics

Duarte, Javier; Li, Haoyang; Roy, Avik; Zhu, Ruike; Huerta, E. A.; Diaz, Daniel; Harris, Philip; Kansal, Raghav; Katz, Daniel S.; Kavoori, Ishaan H.; Kindratenko, Volodymyr V.; Mokhtar, Farouk; Neubauer, Mark S.; Eon Park, Sang; Quinnan, Melissa; Rusack, Roger; Zhao, Zhizhen

doi:10.1088/2632-2153/ad12e3

FAIR AI models in high energy physics

Journal Article · Fri Dec 29 00:00:00 EST 2023 · Machine Learning: Science and Technology

DOI:https://doi.org/10.1088/2632-2153/ad12e3· OSTI ID:2267602

; ; ; Zhu, Ruike; ; ; ; ; ; Kavoori, Ishaan H.; ; ; ; ; ; ; Zhao, Zhizhen

Abstract

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 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.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC02-06CH11357; SC0021225; SC0021258; SC0021395; SC0021396

OSTI ID:: 2267602

Alternate ID(s):: OSTI ID: 2228782
OSTI ID: 2280786

Journal Information:: Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 4 Vol. 4; ISSN 2632-2153

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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