Learning BPS Spectra and the Gap Conjecture

Autor:	Gukov, Sergei, Seong, Rak-Kyeong
Rok vydání:	2024
Předmět:	High Energy Physics - Theory Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing Mathematical Physics Mathematics - Geometric Topology
Zdroj:	Phys. Rev. D 110, 046016 (2024)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevD.110.046016
Popis:	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. Comment: 11 pages, 4 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.09993 Zobrazit plný text záznamu View this record from Arxiv