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High energy nuclear physics meets Machine Learning
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Though being seemingly disparate and with relatively new intersection, high energy nuclear physics and machine learning have already begun to merge and yield interesting results during the last few years. It's worthy to raise the profile of utilizing this novel mindset from machine learning in high energy nuclear physics, to help more interested readers see the breadth of activities around this intersection. The aim of this mini-review is to introduce to the community the current status and report an overview of applying machine learning for high energy nuclear physics, to present from different aspects and examples how scientific questions involved in high energy nuclear physics can be tackled using machine learning.
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Cited by 3 Pith papers
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NNStar: An end-to-end AI agent for nuclear matter and neutron star physics
NNStar packages the RMF-to-neutron-star pipeline as a portable LLM-agent skill, validated on TM1/NL3/FSU-δ6.7 and demonstrated by an autonomous σ6-extended TM1 fit.
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Heavy Quarkonium Spectrum and Decay Constants from a Neural-Network-Based Holographic Model
A neural-network-parametrized dilaton field reproduces the masses and leptonic decay constants of charmonium and bottomonium with 1.26% and 3.32% RMS errors, but only because those values were used as training data.
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Machine Learning Insights into Quark-Antiquark Interactions: Probing Field Distributions and String Tension in QCD
A machine-learning fit to lattice chromo field data yields a compact two-variable expression for E(d, xt) and reproduces flux tube string tension and width over existing separations.
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