Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery
hep-ph, cs.AI, hep-lat, hep-th, nucl-th
Submitted: 2026-10-08
Updated: 2026-10-08
Comments: 37 pages, 22 figures, NST accepted
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- High energy nuclear physics meets Machine Learning
- The BEST framework for the search for the QCD critical point and the chiral magnetic effect
- The QCD phase diagram and Beam Energy Scan physics: a theory overview
- Relativistic Fluid Dynamics In and Out of Equilibrium -- Ten Years of Progress in Theory and Numerical Simulations of Nuclear Collisions
- Gauge/String Duality, Hot QCD and Heavy Ion Collisions
- Bayesian parameter estimation for relativistic heavy-ion collisions
- Exploring QCD matter in extreme conditions with Machine Learning
- Phase Transition Study meets Machine Learning
- Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
- An equation-of-state-meter of QCD transition from deep learning
- Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
- Identifying the nature of the QCD transition in heavy-ion collisions with deep learning
- A machine learning study to identify spinodal clumping in high energy nuclear collisions
- Probing criticality with deep learning in relativistic heavy-ion collisions
- Nuclear liquid-gas phase transition with machine learning
- A fast centrality-meter for heavy-ion collisions at the CBM experiment
- An equation-of-state-meter for CBM using PointNet
- Machine learning study to identify collective flow in small and large colliding systems
- Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer
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