论文标题
深度学习和K-均值聚集在杂质弦真空吸尘器中
Deep learning and k-means clustering in heterotic string vacua with line bundles
论文作者
论文摘要
我们将深度学习技术应用于弦乐景观，特别是$ SO（32）$杂种弦理论，这些理论与线条捆绑包的简单连接的Calabi-yau三倍。事实证明，深度自动编码器网络和K-Means ++聚类指定的三代模型集群。尤其是，我们探索了模型参数与群集之间的相互关系，该群集具有最密集的三代模型（称为“ 3代岛”）。我们发现，三代岛屿与卡拉比三倍的拓扑数据有很强的相关性，尤其是卡拉比奶酪三倍的切线束的第二个切尔恩类。我们的结果还预测了3代岛上的大量希格斯对。
We apply deep-learning techniques to the string landscape, in particular, $SO(32)$ heterotic string theory on simply-connected Calabi-Yau threefolds with line bundles. It turns out that three-generation models cluster in particular islands specified by deep autoencoder networks and k-means++ clustering. Especially, we explore mutual relations between model parameters and the cluster with densest three-generation models (called "3-generation island"). We find that the 3-generation island has a strong correlation with the topological data of Calabi-Yau threefolds, in particular, second Chern class of the tangent bundle of the Calabi-Yau threefolds. Our results also predict a large number of Higgs pairs in the 3-generation island.
