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Machine learning BPS spectra and the gap conjecture

Gukov, Sergei·Seong, Rak-Kyeong
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Abstract

We explore statistical properties of Bogomol’nyi-Prasad-Sommerfield q-series for strongly coupled supersymmetric theories that correspond to a particular family of three-manifolds. We discover that gaps between exponents in the -series are statistically more significant at the beginning of the -series compared to gaps that appear in higher powers of. Our observations are obtained by calculating saliencies of -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.

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97 MATHEMATICS AND COMPUTINGField & string theory models & techniquesGeometryMachine learningSupersymmetric field theoriesTopology

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Gukov, Sergei, Seong, Rak-Kyeong. 2024-08-16. Machine learning BPS spectra and the gap conjecture. https://doi.org/10.1103/physrevd.110.046016

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