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Getting CICY High

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arxiv 1903.03113 v1 pith:NQMMGSTL submitted 2019-03-07 hep-th

Kieran Bull , Yang-Hui He , Vishnu Jejjala , Challenger Mishra This is my paper · ORCID
classification hep-th
keywords cicygeometrieslearningmachinenumberpredicttrainingaccuracy
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abstract

Supervised machine learning can be used to predict properties of string geometries with previously unknown features. Using the complete intersection Calabi-Yau (CICY) threefold dataset as a theoretical laboratory for this investigation, we use low $h^{1,1}$ geometries for training and validate on geometries with large $h^{1,1}$. Neural networks and Support Vector Machines successfully predict trends in the number of K\"ahler parameters of CICY threefolds. The numerical accuracy of machine learning improves upon seeding the training set with a small number of samples at higher $h^{1,1}$.

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Cited by 1 Pith paper

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    An autoencoder and two neural classifiers, trained only on anomaly Gram matrices, provide automated clustering, outlier detection, and consistency predictions for millions of 6d supergravity building blocks.

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