Journal article

Machine learning Calabi-Yau four-folds

Abstract:: Hodge numbers of Calabi-Yau manifolds depend non-trivially on the underlying manifold data and they present an interesting challenge for machine learning. In this letter we consider the data set of complete intersection Calabi-Yau four-folds, a set of about $900, 000 <...>$
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

He, Y.-H., & Lukas, A. (2021). Machine learning Calabi-Yau four-folds. Physics Letters B, 815.

MLA Style

He, Y.-H., and A. Lukas. “Machine Learning Calabi-Yau Four-Folds.” Physics Letters B, vol. 815, Elsevier, 2021.

Chicago Style

He, Y-H, and A Lukas. 2021. “Machine Learning Calabi-Yau Four-Folds.” Physics Letters B 815.
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Files:: He_and_Lukas_2021_machine_learning_calabi_yau_four_folds.pdf

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Publisher copy:: 10.1016/j.physletb.2021.136139

Authors

+ He, Y-H More by this author

Role:: Author
ORCID:: 0000-0002-0787-8380

+ Lukas, A More by this author

Institution:: University of Oxford
Division:: MPLS
Department:: Physics
Sub department:: Theoretical Physics
Role:: Author
ORCID:: 0000-0003-4969-0447

Publisher:: Elsevier
Journal:: Physics Letters B More from this journal
Volume:: 815
Article number:: 136139
Publication date:: 2021-02-12
Acceptance date:: 2021-02-10
DOI:: 10.1016/j.physletb.2021.136139
ISSN:: 0370-2693

Language:: English
Keywords:: FFR
Pubs id:: 1163532
Local pid:: pubs:1163532
Deposit date:: 2021-02-25

Terms of use

Copyright holder:: He and Lukas
Copyright date:: 2021
Rights statement:: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Licence:: CC Attribution (CC BY)

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