Getting CICY High

Understand

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 $h^{1,1}$.