Heterotic String Model Building with Monad Bundles and Reinforcement Learning

Abstract

We use reinforcement learning as a means of constructing string compactifications with prescribed properties. Specifically, we study heterotic SO(10) GUT models on Calabi-Yau three-folds with monad bundles, in search of phenomenologically promising examples. Due to the vast number of bundles and the sparseness of viable choices, methods based on systematic scanning are not suitable for this class of models. By focusing on two specific manifolds with Picard numbers two and three, we show that reinforcement learning can be used successfully to explore monad bundles. Training can be accomplished with minimal computing resources and leads to highly efficient policy networks. They produce phenomenologically promising states for nearly 100% of episodes and within a small number of steps. In this way, hundreds of new candidate standard models are found.

arXiv comment35 pages, 9 figures, data set of models included as ancillary material in the submission

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Cited by in corpus (10)

• Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
• Applying machine learning to the Calabi-Yau orientifolds with string vacua
• Breeding realistic D-brane models
• Ising Machines for Diophantine Problems in Physics
• Free Quotients of Favorable Calabi-Yau Manifolds
• Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
• Constructing and Machine Learning Calabi-Yau Five-folds
• Generating Functions for Line Bundle Cohomology Dimensions on Complex Projective Varieties
• String Model Building, Reinforcement Learning and Genetic Algorithms
• Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning