Speaker
Description
The process of neutrino model building using flavor symmetries requires a physicist to select a group, determine field content, assign representations, construct the Lagrangian, calculate the mass matrices matrix, and perform statistical fits of the resulting free parameters. This process is constrained by the physicist's time and their intuition regarding mathematically complex groups, developed over years of experience. We develop an Autonomous Model Builder (AMBer), capable of performing all of these steps and finding elegant neutrino models using Reinforcement Learning (RL). AMBer is able to minimize the free parameters in the theory while maximizing compatibility with experimental observations. With AMBer, we can explore new groups for neutrino model building that have not been previous considered, and for which neutrino physicists have yet to build intuition, in a fraction of To make this computationally scalable, we re-designed a physics software package and trained AMBer on a supercomputer. We also design visualization tools to understand how AMBer explores the theory space. This serves as a blueprint for scaling AMBer to even more complex theory spaces and use more sophisticated physics software in the future.
Significance
We design a novel technique to rapidly and automatically search and design new theory models in a broader mathematical space than previously possible.
References
Paper will appear on arXiv before the conference
