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

References in corpus (11)

• Four-dimensional String Compactifications with D-Branes, Orientifolds and Fluxes
• A Mini-Landscape of Exact MSSM Spectra in Heterotic Orbifolds
• Stringy origin of non-Abelian discrete flavor symmetries
• Statistics on the Heterotic Landscape: Gauge Groups and Cosmological Constants of Four-Dimensional Heterotic Strings
• Machine Learning of Calabi-Yau Volumes
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• Low Energy Supersymmetry from the Heterotic Landscape
• Geography of Fields in Extra Dimensions: String Theory Lessons for Particle Physics
• Getting CICY High
• Fighting the Floating Correlations: Expectations and Complications in Extracting Statistical Correlations from the String Theory Landscape
• Topological Data Analysis for the String Landscape