Numerical analysis of neutrino physics within a high scale supersymmetry model via machine learning

TL;DR

This paper employs machine learning to numerically analyze neutrino properties within a high scale supersymmetry model, revealing neutrino mass ordering and effective Majorana mass predictions.

Contribution

It introduces a machine learning approach to analyze complex lepton mass matrices in high scale SUSY models, which are analytically intractable.

Findings

Neutrinos are normally ordered.

Effective Majorana mass is about 7×10^{-3} eV.

Neutrino mixing parameters are obtained.

Abstract

A machine learning method is applied to analyze lepton mass matrices numerically. The matrices were obtained within a framework of high scale SUSY and a flavor symmetry, which are too complicated to be solved analytically. In this numerical calculation, the heuristic method in machine learning is adopted. Neutrino masses, mixings, and CP violation are obtained. It is found that neutrinos are normally ordered and the favorable effective Majorana mass is about $7\times 10^{-3}$ eV.