Machine learning complete intersection Calabi-Yau 3-folds
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915043172417536 |
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| author | Keita, Kaniba Mady |
| author_facet | Keita, Kaniba Mady |
| contents | 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 RMSE = 0.0002895011. As for the calibration set, these two parameters are as follows: R^2 = 0.9999999994 and RMSE = 0.0002854348. The training error and the cross-validation error of this regression are 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 RMSE = 7.395731 * 10^(-5) for the validation set of the Gaussian Process regression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11710 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning complete intersection Calabi-Yau 3-folds Keita, Kaniba Mady High Energy Physics - Theory 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 RMSE = 0.0002895011. As for the calibration set, these two parameters are as follows: R^2 = 0.9999999994 and RMSE = 0.0002854348. The training error and the cross-validation error of this regression are 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 RMSE = 7.395731 * 10^(-5) for the validation set of the Gaussian Process regression. |
| title | Machine learning complete intersection Calabi-Yau 3-folds |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2404.11710 |