Learning BPS Spectra and the Gap Conjecture

Abstract

We explore statistical properties of BPS q-series for 3d N=2 strongly coupled supersymmetric theories that correspond to a particular family of 3-manifolds Y. We discover that gaps between exponents in the q-series are statistically more significant at the beginning of the q-series compared to gaps that appear in higher powers of q. Our observations are obtained by calculating saliencies of q-series features used as input data for principal component analysis, which is a standard example of an explainable machine learning technique that allows for a direct calculation and a better analysis of feature saliencies.

11 pages, 4 figures, 3 tables

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• On the Geometry of the String Landscape and the Swampland
• Machine Learning of Calabi-Yau Volumes
• Machine Learning in the String Landscape
• Evolving neural networks with genetic algorithms to study the String Landscape
• Unsupervised Machine Learning Techniques for Exploring Tropical Coamoeba, Brane Tilings and Seiberg Duality
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