Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery

2026-10-08

AI Summary

This study addresses the limitations of conventional machine learning in high-energy nuclear physics, where the absence of physical constraints undermines the reliability of scientific conclusions. To overcome this, we construct a physics-integrated workflow that embeds symmetries and conservation laws into data analysis and simulation. Moving beyond purely architecture-driven approaches, our methodology synergizes Bayesian inference, generative event modeling, differentiable inverse solvers, gauge-equivariant networks, and diffusion-based lattice sampling. This establishes an AI-assisted discovery paradigm centered on physical constraints and uncertainty quantification. The proposed framework effectively enables the extraction of QCD matter properties and signal reconstruction, significantly enhancing the reliability and physical consistency of analytical results across heavy-ion collisions, neutron star physics, and holographic QCD.

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.

Problem

• High-Energy Nuclear Physics
• Physics-Integrated Machine Learning
• Uncertainty Quantification
• Physical Constraints
• Scientific Discovery

Innovation

• Physics-integrated machine learning
• Bayesian inference
• Generative modeling
• Gauge-equivariant networks
• Uncertainty quantification