Machine Learning in Nuclear Physics
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Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.
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Cited by 4 Pith papers
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Probing Proton Structure via Physics-Guided Neural Networks in Holographic QCD
A physics-guided neural network embedding AdS5 Dirac equation and holographic Pomeron fits SLAC proton F2 data with chi-squared per degree of freedom of 0.91 and identifies a kinematic crossover at x approximately 0.1...
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Extraction of the color dipole amplitude with physics-informed neural networks
Physics-informed neural networks extract a model-independent color dipole amplitude from inclusive HERA data that predicts exclusive J/ψ photoproduction cross-sections without parameter retuning.
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A flow-matching generative model for event-by-event jet-induced hydro response in high-energy heavy-ion collisions
A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data s...
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Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit
A DNN solves the symmetrized spectator form of the subtracted Faddeev equations for three identical bosons, reproducing Efimov binding scales at unitarity to within 0.022% and tracing bound-state branches versus inver...
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