Researchers have developed a novel interpretable Multitask deep-learning framework to analyze ultra-peripheral collisions (UPCs) for a more precise understanding of nuclear structure. This framework maps transverse momentum distributions to various nuclear-structure indicators, identifying key kinematic regions for inference. The approach was demonstrated using coherent $J/\psi$ photoproduction in $^{96}{40}Zr + ^{96}{40}Zr$ collisions, effectively separating diffraction-dominated and interference-dominated information. AI
IMPACT Introduces a novel interpretable deep learning framework for nuclear structure analysis, potentially advancing scientific discovery.
RANK_REASON The cluster describes a scientific paper detailing a new deep learning framework for nuclear physics research.
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- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- J/": Photoproduction
- J/psi meson
- Multitask Deep Learning for Joint Detection of Necrotizing Viral and Noninfectious Retinitis From Common Blood and Serology Test Data
- ScienceCast
- Ultra-peripheral collisions and hadronic structure
- Zr
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