Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality

TL;DR

This paper applies unsupervised machine learning methods like PCA and t-SNE to analyze the space of toric phases in 4d N=1 supersymmetric gauge theories, revealing phase boundaries related to Seiberg duality through coamoeba projections.

Contribution

It introduces a novel application of unsupervised learning to identify and visualize toric phases and Seiberg duality in brane tilings of Calabi-Yau 3-folds.

Findings

Successfully visualized phase boundaries in a 2D phase diagram.

Demonstrated the effectiveness of PCA and t-SNE in analyzing complex moduli spaces.

Provided insights into the structure of toric phases and dualities.

Abstract

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.