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.

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

References in corpus (8)

• Heterotic GUT and Standard Model Vacua from simply connected Calabi-Yau Manifolds
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• A Heterotic Standard Model with B-L Symmetry and a Stable Proton
• Hypercharge Flux in Heterotic Compactifications
• Revealing systematics in phenomenologically viable flux vacua with reinforcement learning
• Swampland Conjectures and Infinite Flop Chains
• Cohomology Chambers on Complex Surfaces and Elliptically Fibered Calabi-Yau Three-folds
• Particle Physics Model Building with Reinforcement Learning

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• 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