Quiver Mutations, Seiberg Duality and Machine Learning

TL;DR

This paper explores applying machine learning to classify and determine dualities in quiver gauge theories related to Seiberg duality, with high accuracy achieved in various classification tasks.

Contribution

It introduces the use of machine learning techniques to analyze Seiberg duality in quiver gauge theories, including binary and multi-class classification, and compares different models and data features.

Findings

High accuracy in duality classification tasks

Naive Bayes and CNNs show different advantages

Additional data improves model performance

Abstract

We initiate the study of applications of machine learning to Seiberg duality, focusing on the case of quiver gauge theories, a problem also of interest in mathematics in the context of cluster algebras. Within the general theme of Seiberg duality, we define and explore a variety of interesting questions, broadly divided into the binary determination of whether a pair of theories picked from a series of duality classes are dual to each other, as well as the multi-class determination of the duality class to which a given theory belongs. We study how the performance of machine learning depends on several variables, including number of classes and mutation type (finite or infinite). In addition, we evaluate the relative advantages of Naive Bayes classifiers versus Convolutional Neural Networks. Finally, we also investigate how the results are affected by the inclusion of additional data, such as ranks of gauge/flavor groups and certain variables motivated by the existence of underlying Diophantine equations. In all questions considered, high accuracy and confidence can be achieved.