论文标题
通过机器学习的高度超对称模型中中微子物理的数值分析
Numerical analysis of neutrino physics within a high scale supersymmetry model via machine learning
论文作者
论文摘要
使用机器学习方法来分析Lepton质量矩阵数值。这些矩阵是在高度SUSY和风味对称性的框架内获得的，这些矩阵太复杂了，无法在分析上解决。在此数值计算中，采用了机器学习中的启发式方法。获得了中微子肿块，混合和CP违规。发现正常订购中微子，有效的有效Majorana质量约为$ 7 \ times 10^{ - 3} $ ev。
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.
