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LEE, Seung Joo
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Machine learning string standard models

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Title
Machine learning string standard models
Author(s)
Rehan Deen; Yang-Hui He; Seung-Joo Lee; Andre Lukas
Publication Date
2022-02
Journal
Physical Review d, v.105, no.4
Publisher
AMER PHYSICAL SOC
Abstract
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 autoencoder. Learning nontopological properties, specifically the number of Higgs multiplets, turns out to be more difficult, but is possible using sizeable networks and feature-enhanced datasets.
URI
https://pr.ibs.re.kr/handle/8788114/11177
DOI
10.1103/PhysRevD.105.046001
ISSN
2470-0010
Appears in Collections:
Center for Fundamental Theory(순수물리이론 연구단) > 1. Journal Papers (저널논문)
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