FAIR4HEP is a joint DOE-funded venture between UIUC, MIT, UMN, and UCSD. The goal of the multi-institution and interdisciplinary project is to curate data sources from HEP, develop software frameworks to automatically train, evaluate, and compare benchmark AI models for charged particle tracking, Higgs boson identification, detector calibration, event reconstruction, and more, and publish sharable AI models and data following FAIR (findable, accessible, interoperable, and reusable) principles.
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Transformational progress in AI has been driven by the ubiquity of large datasets such as ImageNe0t. Within HEP, creating and publishing open, realistic, and FAIR datasets can shed light on the unique challenges in this domain and usher in new groundbreaking and physics-inspired ideas in AI.