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Machine learning string standard models

Deen, R., He, Y-H. ORCID: 0000-0002-0787-8380, Lee, S-J. & Lukas, A. (2022). Machine learning string standard models. Physical Review D (PRD), 105(4), 046001. doi: 10.1103/PhysRevD.105.046001


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.

Publication Type: Article
Additional Information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP.
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Mathematics
Text - Published Version
Available under License Creative Commons: Attribution International Public License 4.0.

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