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Learning BPS Spectra and the Gap Conjecture

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arxiv 2405.09993 v1 pith:OSI5NVT7 submitted 2024-05-16 hep-th cs.LGcs.NEmath-phmath.GTmath.MP

Learning BPS Spectra and the Gap Conjecture

classification hep-th cs.LGcs.NEmath-phmath.GTmath.MP
keywords q-seriesanalysisgapslearningsalienciesallowsappearbeginning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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  1. $c_{\rm eff}$ from Resurgence at the Stokes Line

    hep-th 2025-08 unverdicted novelty 6.0

    Resurgent cyclic orbits' algebraic structure plus the leading q-series term determines the asymptotic growth exponent of dual q-series coefficients, which equals an effective central charge c_eff in a related 3d N=2 QFT.

  2. Pre-Strings Lectures on Artificial Intelligence

    hep-th 2026-07 accept novelty 5.5

    Lecture notes define neural-network field theory and survey how it recovers known QFT/string results plus applied AI techniques for string problems.