Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality
This work addresses a specific problem in theoretical physics and string theory by applying machine learning to explore Seiberg duality in supersymmetric gauge theories, representing an incremental application of existing methods to a new domain.
The authors tackled the problem of identifying toric phases of 4d N=1 supersymmetric gauge theories related to toric Calabi-Yau 3-folds by using unsupervised machine learning techniques like PCA and t-SNE to project coamoeba spaces into lower-dimensional phase diagrams, illustrating this with a 2D phase diagram for brane tilings of the cone over the Hirzebruch surface F0.
We introduce unsupervised machine learning techniques in order to identify toric phases of 4d N=1 supersymmetric gauge theories corresponding to the same toric Calabi-Yau 3-fold. These 4d N=1 supersymmetric gauge theories are worldvolume theories of a D3-brane probing a toric Calabi-Yau 3-fold and are realized in terms of a Type IIB brane configuration known as a brane tiling. It corresponds to the skeleton graph of the coamoeba projection of the mirror curve associated to the toric Calabi-Yau 3-fold. When we vary the complex structure moduli of the mirror Calabi-Yau 3-fold, the coamoeba and the corresponding brane tilings change their shape, giving rise to different toric phases related by Seiberg duality. We illustrate that by employing techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), we can project the space of coamoeba labelled by complex structure moduli down to a lower dimensional phase space with phase boundaries corresponding to Seiberg duality. In this work, we illustrate this technique by obtaining a 2-dimensional phase diagram for brane tilings corresponding to the cone over the zeroth Hirzebruch surface F0.