Brain webs for brane webs
Brain webs for brane webs
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DOI：
10.1016/j.physletb.2022.137376
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发表时间：
2022-02
影响因子：
4.4
通讯作者：
G. Arias-Tamargo;Yang-Hui He;Elli Heyes;Edward Hirst;D. Rodríguez-Gómez
中科院分区：
文献类型：
--
作者：
G. Arias-Tamargo;Yang-Hui He;Elli Heyes;Edward Hirst;D. Rodríguez-Gómez
我们提出了一种新技术，用于对 IIB 型弦理论中的膜网产生的 5d 超共形场论进行分类，使用机器学习技术来识别产生相同理论的不同膜。我们专注于具有三个外腿的网，其问题类似于对 7 膜组进行分类的问题。训练连体神经网络来确定任意两个膜网之间的等效性，当在较弱的条件下将膜网视为等效时，性能会得到改善。因此，这表明 7 膜组的推测分类是不完整的。
We propose a new technique for classifying 5d Superconformal Field Theories arising from brane webs in Type IIB String Theory, using technology from Machine Learning to identify different webs giving rise to the same theory. We concentrate on webs with three external legs, for which the problem is analogous to that of classifying sets of 7-branes. Training a Siamese Neural Network to determine equivalence between any two brane webs shows an improved performance when webs are considered equivalent under a weaker set of conditions. This therefore suggests that the conjectured classification of 7-brane sets is not complete.