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arXiv · 2508.19157

Machine Learning Free Quotients of CICYs

Wei Cui·Xin Gao·Mohsen Karkheiran·Juntao Wang
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Abstract

Free quotients of Calabi-Yau manifolds play an important role in string compactification. In this paper, we explore machine learning techniques, such as fully connected neural networks and multi-head attention (MHA) models, as a potential approach to detect $\mathbb{Z}_2$, $\mathbb{Z}_3$, $\mathbb{Z}_4$ and $\mathbb{Z}_2\times\mathbb{Z}_2$ free quotients of CICYs. When tested on unseen examples, both models successfully identified almost all free quotients for $\mathbb{Z}_2$, $\mathbb{Z}_3$, $\mathbb{Z}_4$ and $\mathbb{Z}_2\times\mathbb{Z}_2$ symmetry. These results demonstrate that well-trained machine learning models can effectively generalize to new Calabi-Yau manifolds and may aid in the broader classification of free quotients in the future.

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Wei Cui, Xin Gao, Mohsen Karkheiran, Juntao Wang. 2025-08-26. Machine Learning Free Quotients of CICYs. https://arxiv.org/abs/2508.19157

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