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
Related papers
- Classification of g-modes for neutron stars with a strong transition: Novel universal relation including slow stable hybrid stars
- Higgsino Dark Matter Interpretation of the LUX-ZEPLIN 248 keV Nuclear-Recoil Event
- A Unified Bogoliubov Approach to Primordial Gravitational Waves: From Inflation to Reheating
- Probing Memory-Burdened Primordial Black Holes with High-Energy Neutrinos
- Enhanced Dark Matter Quantum Sensing via Phase-Space Geometric Interferometry
- Axions as Dark Matter, Dark Energy, and Dark Radiation