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Breeding Realistic D-Brane Models
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DOI：10.1002/prop.202200038.png)
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Intersecting branes provide a useful mechanism to construct particle physics models from string theory with a wide variety of desirable characteristics. The landscape of such models can be enormous, and navigating towards regions which are most phenomenologically interesting is potentially challenging. Machine learning techniques can be used to efficiently construct large numbers of consistent and phenomenologically desirable models. In this work we phrase the problem of finding consistent intersecting D-brane models in terms of genetic algorithms, which mimic natural selection to evolve a population collectively towards optimal solutions. For a four-dimensional N=1${\cal N}=1$ supersymmetric type IIA orientifold with intersecting D6-branes, we demonstrate that O(106)$\mathcal {O}(10<^>6)$ unique, fully consistent models can be easily constructed, and, by a judicious choice of search environment and hyper-parameters, O(30%)$\mathcal {O}(30\%)$ of the found models contain the desired Standard Model gauge group factor. Having a sizable sample allows us to draw some preliminary landscape statistics of intersecting brane models both with and without the restriction of having the Standard Model gauge factor.
Keyword:
genetic algorithms
machine learning and data science
string landscape
string theory and D-brane models
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