论文标题
预测MSSM的Orbifold起源
Predicting the orbifold origin of the MSSM
论文作者
论文摘要
从圆环Orbifolds（类型的$ t^6/p $）上的杂丝弦的紧凑型中的MSSM样弦模型具有独特的现象学特性，例如矢量样的外视谱，超对称性破裂的规模以及非亚伯利亚风味的象征。我们表明，这些特征至关重要地取决于基础Orbifold Point Group $ p $的选择。详细说明，我们使用增强的决策树来预测$ p $从类似MSSM的Orbifold模型的现象学特性中。由于这种工作非常出色，我们可以利用机器学习来预测MSSM的Orbifold。
MSSM-like string models from the compactification of the heterotic string on toroidal orbifolds (of the kind $T^6/P$) have distinct phenomenological properties, like the spectrum of vector-like exotics, the scale of supersymmetry breaking, and the existence of non-Abelian flavor symmetries. We show that these characteristics depend crucially on the choice of the underlying orbifold point group $P$. In detail, we use boosted decision trees to predict $P$ from phenomenological properties of MSSM-like orbifold models. As this works astonishingly well, we can utilize machine learning to predict the orbifold origin of the MSSM.
