Heterotic String Model Building with Monad Bundles and Reinforcement Learning

TL;DR

This paper demonstrates that reinforcement learning can efficiently explore heterotic string compactifications with monad bundles on Calabi-Yau manifolds, discovering hundreds of promising models with minimal computational resources.

Contribution

It introduces a novel application of reinforcement learning to heterotic string model building, enabling efficient exploration of large bundle spaces on specific Calabi-Yau manifolds.

Findings

Reinforcement learning successfully finds phenomenologically promising models.

High success rate of nearly 100% in episodes.

Discovery of hundreds of new candidate models.

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.