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Heterotic String Model Building with Monad Bundles and Reinforcement Learning

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arxiv 2108.07316 v1 pith:B73FMBYF submitted 2021-08-16 hep-th cs.LG

Andrei Constantin , Thomas R. Harvey , Andre Lukas This is my paper · ORCID
classification hep-thcs.LG
keywords bundleslearningmodelsmonadreinforcementheteroticnumberphenomenologically
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exploring Line Bundle Standard Models with Transformers

    hep-th 2026-06 unverdicted novelty 7.0 of 10

    A Transformer trained by reinforcement learning generates heterotic line-bundle sums that satisfy anomaly-cancellation, stability, and chirality constraints, and its policy transfers usefully across Calabi-Yau geometries.

  2. Generating particle physics Lagrangians with transformers

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A BART transformer can generate gauge-invariant Lagrangians from field content with over 90% accuracy on in-distribution data, though its performance drops on realistic Standard Model benchmarks.

  3. Pre-Strings Lectures on Artificial Intelligence

    hep-th 2026-07 accept novelty 5.5 of 10

    Lecture notes define neural-network field theory and survey how it recovers known QFT/string results plus applied AI techniques for string problems.

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