Machine learning Calabi–Yau three-folds, four-folds, and five-folds

In this manuscript, we demonstrate, using several regression techniques, that the remaining independent Hodge numbers of complete intersection Calabi–Yau four-folds and five-folds can be machine learned from h 1 , 1 and h 2 , 1 . Consequently, we combine the Hodge numbers h 1 , 1 and h 2 , 1 from the complete intersection Calabi-Yau three-folds, four-folds, and five-folds into a single dataset. We then implement various classification algorithms on this dataset. For example, Gaussian process and naive Bayes classifiers both achieve 100% accuracy in binary classification between three-folds and four-folds. Using the Support Vector Machine (SVM) algorithm, a special corner is identified in the Calabi–Yau four-fold landscape (characterized by 15 ≤ h 1 , 1 ≤ 30 and 95 ≤ h 2 , 1 ≤ 100 ) during multiclass classification. Furthermore, the highest accuracy 1.00000, in classifying Calabi–Yau three-folds, four-folds, and five-folds is obtained using the naive Bayes classifier.