AI for nuclear physics: the EXCLAIM project
Abstract An overview of the recent activity of the newly funded EXCLusives with AI and Machine learning (EXCLAIM) collaboration is presented. The main goal of the collaboration is to develop a framework to implement AI and machine learning techniques in problems emerging from the phenomenology of high energy exclusive scattering processes from nucleons and nuclei, maximizing the information that can be extracted from various sets of experimental data, while implementing theoretical constraints from lattice QCD. A specific perspective embraced by EXCLAIM is to use the methods of theoretical physics to understand the working of ML, beyond its standardized applications to physics analyses which most often rely on industrially provided tools, in an automated way.
Authors
- Matthew D. Sievert (ORCID: https://orcid.org/0000-0002-6018-269X)
- Douglas Adams (ORCID: https://orcid.org/0000-0002-5807-4892)
- Gary R. Goldstein (ORCID: https://orcid.org/0000-0002-0920-8540)
- M. Boër (ORCID: https://orcid.org/0000-0001-9157-4349)
- Simonetta Liuti (ORCID: https://orcid.org/0000-0002-6689-0224)
- Michael Engelhardt (ORCID: https://orcid.org/0000-0002-0472-8862)
- Yang Li (ORCID: https://orcid.org/0000-0001-9446-6503)
- Huey-Wen Lin (ORCID: https://orcid.org/0000-0001-6281-944X)
- B. Kriesten
- Dennis Sivers
- Gia-Wei Chern (ORCID: https://orcid.org/0000-0002-5773-7850)
Institutions
- New Mexico State University (US)
- Tufts University (US)
- Argonne National Laboratory (US)
- University of Michigan (US)
- University of Virginia (US)
- Old Dominion University (US)
- Virginia Tech (US)
- Michigan State University (US)
Publication Details
- Journal
- Journal of Instrumentation
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1088/1748-0221/20/08/c08011
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00