MSSM‐like string models from the compactification of the heterotic string on toroidal orbifolds (of the kind ) 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.

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预测MSSM的起源

来自环形双体上的杂散弦的压缩的类MSSM字符串模型具有类似的现象学特性，例如类似矢量的外来谱，超对称破坏的规模以及非阿贝尔风味对称的存在。我们表明，这些特征至关重要地取决于基础双点组P的选择。详细地说，我们使用增强型决策树从类似于MSSM的球型模型的现象学特性预测P。由于这非常好用，因此我们可以利用机器学习来预测MSSM的起源。