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Deep learning in the heterotic orbifold landscape. (English) Zbl 1409.81099
Summary: We use deep autoencoder neural networks to draw a chart of the heterotic \(\mathbb{Z}_6\)-II orbifold landscape. Even though the autoencoder is trained without knowing the phenomenological properties of the \(\mathbb{Z}_6\)-II orbifold models, it identifies fertile islands in this chart where phenomenologically promising models cluster. Then, we apply a decision tree to our chart in order to extract the defining properties of the fertile islands. Based on this information we propose a new search strategy for phenomenologically promising string models.

MSC:
81T30 String and superstring theories; other extended objects (e.g., branes) in quantum field theory
81V22 Unified quantum theories
57R18 Topology and geometry of orbifolds
20B05 General theory for finite permutation groups
62M45 Neural nets and related approaches to inference from stochastic processes
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