Reinforcement Learning Techniques for the Flavor Problem in Particle Physics
Abstract
This short review discusses recent applications of Reinforcement Learning (RL) techniques to the flavor problem in particle physics. Traditional approaches to fermion masses and mixing often rely on extensions of the Standard Model based on horizontal symmetries, but the vast landscape of possible models makes systematic exploration infeasible. Recent works have shown that RL can efficiently navigate this landscape by constructing models that reproduce observed quark and lepton observables. These approaches demonstrate that RL not only rediscovers models already proposed in the literature but also uncovers new, phenomenologically acceptable solutions.
Fields of Study & Subjects
Particle physics theoretical and experimental studies
Quantum Chromodynamics and Particle Interactions
Neutrino Physics Research
Publication details
- Journal / source
- Symmetry
- Volume
- 18
- Issue
- 1
- Pages
- 131–131
- Publication date
- 9 January 2026
- Language
- EN