Machine Learning Calabi-Yau Three-Folds, Four-Folds, and Five-Folds
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912781087801344 |
|---|---|
| author | Keita, Kaniba Mady Dicko, Younouss Hamèye |
| author_facet | Keita, Kaniba Mady Dicko, Younouss Hamèye |
| contents | 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 $15 \leq h^{1,1} \leq 30$ and $95 \leq h^{2,1} \leq 100$) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00139 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Calabi-Yau Three-Folds, Four-Folds, and Five-Folds Keita, Kaniba Mady Dicko, Younouss Hamèye High Energy Physics - Theory 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 $15 \leq h^{1,1} \leq 30$ and $95 \leq h^{2,1} \leq 100$) 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. |
| title | Machine Learning Calabi-Yau Three-Folds, Four-Folds, and Five-Folds |
| topic | High Energy Physics - Theory |
| url | https://arxiv.org/abs/2503.00139 |