Breeding realistic D-brane models

TL;DR

This paper introduces a genetic algorithm approach to efficiently generate and analyze large sets of consistent intersecting D-brane models in string theory, aiding the search for phenomenologically interesting particle physics models.

Contribution

It applies genetic algorithms to systematically explore the intersecting D-brane landscape, achieving large-scale model generation and preliminary statistical analysis.

Findings

Generated around 1 million consistent models.

Approximately 30% of models contain the Standard Model gauge group.

Provided initial landscape statistics for intersecting brane models.

Abstract

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 ${\cal N}=1$ supersymmetric type IIA orientifold with intersecting D6-branes, we demonstrate that $\mathcal{O}(10^6)$ unique, fully consistent models can be easily constructed, and, by a judicious choice of search environment and hyper-parameters, $\mathcal{O}(30\%)$ 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.