Machine learning complete intersection Calabi-Yau 3-folds
Keita Kaniba Mady · Physical review. D/Physical review. D. · 2024
Gaussian process regression, kernel support vector regression, the random forest, extreme gradient boosting, and the generalized linear model algorithms are applied to data of complete intersection Calabi-Yau threefolds. It is shown that Gaussian process regression is the most suitable for learning the Hodge number h 2 , 1 in terms of h 1 , 1 . The performance of this regression algorithm is such that the Pearson correlation coefficient for the validation set is R 2 = 0.9999999995 with a root mean square error R M S E = 0.0002895011 . As for the train set, these two parameters are as follows: R 2 = 0.9999999994 and R M S E = 0.0002854348 . The training error and the cross-validation error of this regression are 1 × 10 − 9 and 1.28 × 10 − 7 , respectively. Learning the Hodge number h 1 , 1 in terms of h 2 , 1 yields R 2 = 1.000000 and R M S E = 7.395731 × 10 − 5 for the validation set of the Gaussian process regression. Published by the American Physical Society 2024