We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped with a diverse set of tools from the machine-learning toolbox. Unsupervised techniques identify an unanticipated almost linear dependence of the topological data on the weights. This then allows us to identify a previously unnoticed clustering in the Calabi-Yau data. Supervised techniques are successful in predicting the topological parameters of the hypersurface from its weights with an accuracy of R^2 > 95%. Supervised learning also allows us to identify weighted-P4s which admit Calabi-Yau hypersurfaces to 100% accuracy by making use of partitioning supported by the clustering behaviour.
翻译:我们重新查看了典型的加权P4数据库,该数据库接纳了Calabi-Yau 3倍的超表层,这些超表层配备了来自机器学习工具箱的各种工具。 未经监督的技术确定了在重量上不可预见的地貌数据几乎线性依赖性。 这使我们能够在Calabi-Yau数据中找出一个先前未加注意的集群。 受监督的技术成功地从超表层重量中预测了超表层的表层参数, 精确度为R%2 > 95%。 受监督的学习还使我们能够通过使用集束行为支持的分隔法, 确定允许Calabi- Yau 超表层达到100%精确度的加权P4。