Machine learning improves discovery in high-energy nuclear physics research
Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery
Artificial Intelligence
Summary
High-energy nuclear physics studies extreme states of matter that are hard to understand using traditional methods. The authors review how recent machine learning advances help scientists by combining physical laws directly into data analysis and simulations. This integration improves the accuracy of extracting important properties and understanding phenomena like heavy-ion collisions and neutron stars. They highlight how constraints like symmetry and conservation help ensure trustworthy results with quantified uncertainty.
What this means in practice
- •For nuclear physics data teams: Incorporate physics constraints into machine learning workflows to improve extraction of QCD matter properties from experimental and astrophysical data.
- •For computational physicists: Use physics-integrated ML models for simulating lattice field theories and holographic QCD more efficiently with validated uncertainty.
A survey. It maps existing work.
Authors
Xun Chen, Weiyao Ke, Yu-Gang Ma, Long-Gang Pang, Kai Zhou
Abstract
Machine learning (ML) in high-energy nuclear physics (HENP) is entering a new stage in which physical knowledge is incorporated more directly into data analysis, simulation, and physics inference. This mini-review focuses on developments that have matured in the past several years. Whereas earlier applications emphasized event classification, pattern recognition, and surrogate models for selected observables, recent work has moved toward physics-integrated workflows: calibrated Bayesian extraction of QCD matter properties, dense-matter equation-of-state inference from heavy-ion and neutron-star data, generative event modeling, neural unfolding of weak physical signals, differentiable inverse solvers, gauge-equivariant and diffusion-based lattice-field samplers, and neural reconstruction of model functions in holographic QCD. We survey recent applications of ML in heavy-ion collisions, neutron-star physics, lattice QFT, and holographic or continuum QCD. The emphasis is not on ML architectures alone, but on how they enter concrete physics workflows, how physical constraints such as symmetries, conservation laws, causality, thermodynamic stability, and topology are imposed, and how uncertainty quantification and validation determine whether an AI-assisted result can support a reliable physics conclusion.