论文标题
具有任意几何形状的异源孔的自动编码器
An autoencoder for heterotic orbifolds with arbitrary geometry
论文作者
论文摘要
人工神经网络对于改善可接受的弦线压缩并表征它们变得很重要。在本文中，我们构建了杂音轨道编码器，这是一种一般的深层自动编码器，用于研究由各种Abelian Orbifold几何形状引起的杂质孔模型。我们的神经网络可以轻松训练，以成功地编码许多Orbifold几何形状的大参数空间，同时独立于其训练功能的统计差异。特别是，我们表明我们的自动编码器能够精确地压缩两个有前途的Orbifold几何形状的大参数空间，仅需三个参数。此外，大多数具有现象学上吸引人特征的Orbifold模型都出现在这个小空间的有限区域中。我们的贡献暗示了可能简化（有希望的）异源孔模型的分类。
Artificial neural networks have become important to improve the search for admissible string compactifications and characterize them. In this paper we construct the heterotic orbiencoder, a general deep autoencoder to study heterotic orbifold models arising from various Abelian orbifold geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilarities of their training features. In particular, we show that our autoencoder is capable of compressing with good accuracy the large parameter space of two promising orbifold geometries in just three parameters. Further, most orbifold models with phenomenologically appealing features appear in bounded regions of this small space. Our contribution hints towards a possible simplification of the classification of (promising) heterotic orbifold models.
