Heterotic String Model Building with Monad Bundles and Reinforcement Learning
This addresses the challenge of efficiently finding viable string theory models for particle physics, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of constructing heterotic string compactifications with specific properties by using reinforcement learning to explore monad bundles on Calabi-Yau manifolds, resulting in the discovery of hundreds of new candidate standard models with nearly 100% success rate in episodes and minimal computing resources.
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.