Learning BPS Spectra and the Gap Conjecture
<|reference_start|>Learning BPS Spectra and the Gap Conjecture: 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.<|reference_end|>
@article{gukov2024learning,
    title={Learning BPS Spectra and the Gap Conjecture},
    author={Sergei Gukov, Rak-Kyeong Seong},
    journal={Phys. Rev. D 110, 046016 (2024)},
    year={2024},
    doi={10.1103/PhysRevD.110.046016},
    number={UNIST-MTH-24-RS-02},
    archivePrefix={arXiv},
    eprint={2405.09993},
    primaryClass={hep-th cs.LG cs.NE math-ph math.GT math.MP}
}