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2020

Machine Learning String Standard Models

Deen, Rehan, He, Yang-Hui, Lee, Seung-Joo et al.

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We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models.

Both supervised and unsupervised learning are considered.
We find that, for a fixed compactification manifold, relatively small neural networks are capable of distinguishing consistent line bundle models with the correct gauge group and the correct chiral asymmetry from random models without these properties.
The same distinction can also be achieved in the context of unsupervised learning, using an auto-encoder.

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Then

J. Carifio, J. Halverson, D. Krioukov and B. D. Nelson, JHEP
Original
2017
Later among the works it cites.
L. B. Anderson, X. Gao, J. Gray and S. J. Lee, “Fibrations in CICY Threefolds,”
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2017
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A. Constantin, Y. H. He and A. Lukas, “Counting String Theory Standard Models,”
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A. Mutter, E. Parr and P. K. S. Vaudrevange, “Deep learning in the heterotic orbifold landscape,”
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A. Constantin and A. Lukas, “Formulae for Line Bundle Cohomology on Calabi?Yau Threefolds,”
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D. Krefl and R. K. Seong, Phys. Rev. D

2017

Cited alongside, same era.

F. Ruehle, “Evolving neural networks with genetic algorithms to study the String Landscape,”

2017

Cited alongside, same era.

Y. H. He, “Deep-Learning the Landscape,” arXiv:1706.02714

Cited in the paper.

Y. H. He, “The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning,” arXiv:1812.02893 [hep-th]

Cited in the paper.

T. Hübsch, private communication

Cited in the paper.

Heterotic line bundle data can be downloaded at

Cited in the paper.

2019

Later among the works it cites.

D. Klaewer and L. Schlechter, “Machine Learning Line Bundle Cohomologies of Hypersurfaces in Toric Varieties,”

2019

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F. Ruehle, “Data science applications to string theory,”

2020

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