FAIR AI Models in High Energy Physics
This work addresses the need for standardized and reusable AI models in high energy physics, representing an incremental step in adapting FAIR principles to machine learning.
The paper tackles the challenge of applying FAIR principles to AI models in high energy physics by proposing a practical definition and template, demonstrating it with a graph neural network for Higgs boson identification, and reporting on robustness, portability, and interpretability.
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.