Tootfinder

Towards AI-assisted Neutrino Flavor Theory Design
Jason Benjamin Baretz, Max Fieg, Vijay Ganesh, Aishik Ghosh, V. Knapp-Perez, Jake Rudolph, Daniel Whiteson
https://arxiv.org/abs/2506.08080

Towards AI-assisted Neutrino Flavor Theory Design
Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model's construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop an Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a …