Brain Webs for Brane Webs

TL;DR

This paper introduces a machine learning approach, specifically Siamese Neural Networks, to classify 5d Superconformal Field Theories from brane webs, revealing incomplete existing classifications and improving understanding of web equivalences.

Contribution

It applies machine learning to classify brane webs, demonstrating that current theoretical classifications are incomplete and providing a new computational perspective.

Findings

Machine learning improves classification accuracy of brane webs.

Web equivalence under weaker conditions enhances classification.

Explicit examples confirm incomplete existing classifications.

Abstract

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. Thus, Machine Learning teaches us that the conjectured classification of 7-brane sets is not complete, which we confirm with explicit examples.