Contrast data mining for the MSSM from strings

Abstract

We apply techniques from data mining to the heterotic orbifold landscape in order to identify new MSSM-like string models. To do so, so-called contrast patterns are uncovered that help to distinguish between areas in the landscape that contain MSSM-like models and the rest of the landscape. First, we develop these patterns in the well-known ${\mathbb{Z}}_6$-II orbifold geometry and then we generalize them to all other ${\mathbb{Z}}_N$ orbifold geometries. Our contrast patterns have a clear physical interpretation and are easy to check for a given string model. Hence, they can be used to scale down the potentially interesting area in the landscape, which significantly enhances the search for MSSM-like models. Thus, by deploying the knowledge gain from contrast mining into a new search algorithm we create many novel MSSM-like models, especially in corners of the landscape that were hardly accessible by the conventional search algorithm, for example, MSSM-like ${\mathbb{Z}}_6$-II models with $\Delta (54)$ flavor symmetry.

34 pages, 9 figures

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Cited by in corpus (9)

• Lessons from eclectic flavor symmetries
• The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
• Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
• The landscape of promising non-supersymmetric string models
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• Doublet-Triplet Splitting in Fertile Left-Right Symmetric Heterotic String Vacua
• Universes as Big Data
• Metastable vacua from torsion and machine learning
• Testing Swampland Conjectures with Machine Learning