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AI for Nuclear Physics: the EXCLAIM project

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arxiv 2408.00163 v2 pith:OBPRBF66 submitted 2024-07-31 hep-ph

AI for Nuclear Physics: the EXCLAIM project

classification hep-ph
keywords exclaimphysicscollaborationlearningmachinetheoreticalactivityanalyses
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In 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.

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  1. GTMDs, orbital angular momentum, and pretzelosity

    hep-ph 2026-05 unverdicted novelty 4.0

    In the bag model, GTMD calculations are consistent, orbital angular momentum is tied to F_{1,4}^q through the Ji sum rule, and a deeper link to pretzelosity TMD is established.