Distinguishing Calabi-Yau Topology using Machine Learning

Fuente: arXiv
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Main Authors: He, Yang-Hui, Yao, Zhi-Gang, Yau, Shing-Tung
Format: Preprint
Published: 2024
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author He, Yang-Hui
Yao, Zhi-Gang
Yau, Shing-Tung
author_facet He, Yang-Hui
Yao, Zhi-Gang
Yau, Shing-Tung
contents While the earliest applications of AI methodologies to pure mathematics and theoretical physics began with the study of Hodge numbers of Calabi-Yau manifolds, the topology type of such manifold also crucially depend on their intersection theory. Continuing the paradigm of machine learning algebraic geometry, we here investigate the triple intersection numbers, focusing on certain divisibility invariants constructed therefrom, using the Inception convolutional neural network. We find $\sim90\%$ accuracies in prediction in a standard fivefold cross-validation, signifying that more sophisticated tasks of identification of manifold topologies can also be performed by machine learning.
format Preprint
id arxiv_https___arxiv_org_abs_2408_05076
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Distinguishing Calabi-Yau Topology using Machine Learning
He, Yang-Hui
Yao, Zhi-Gang
Yau, Shing-Tung
Algebraic Geometry
While the earliest applications of AI methodologies to pure mathematics and theoretical physics began with the study of Hodge numbers of Calabi-Yau manifolds, the topology type of such manifold also crucially depend on their intersection theory. Continuing the paradigm of machine learning algebraic geometry, we here investigate the triple intersection numbers, focusing on certain divisibility invariants constructed therefrom, using the Inception convolutional neural network. We find $\sim90\%$ accuracies in prediction in a standard fivefold cross-validation, signifying that more sophisticated tasks of identification of manifold topologies can also be performed by machine learning.
title Distinguishing Calabi-Yau Topology using Machine Learning
topic Algebraic Geometry
url https://arxiv.org/abs/2408.05076