Data analysis of millions of Type IIB flux vacua
Abstract
We study the vacuum structure of flux compactifications of type IIB string theory on the degree 18 hypersurface in ℂℙ41,1,1,6,9 with two effective complex structure moduli. We scan 30,770,178 flux choices compatible with the tadpole bound, and use a gradient-based constrained minimization based on the SLSQP algorithm to identify candidates for local minima of the scalar potential, yielding a dataset of millions of candidate flux vacua. Using the 1,296,633 reliable solutions selected from this dataset, we analyze distributions and correlations of various physical quantities such as vacuum expectation values of moduli and vacuum energy. Using quadratic flux invariants as input features, we train a classifier to identify solutions satisfying the numerical SUSY criterion |V|<10−4.5. On a randomly selected test set, selecting the top-ranked 5% recovers 47.2% of the SUSY test solutions. We also use supervised regression to predict the potential and moduli field values. In the regression comparison, quadratic flux invariant inputs improve prediction of potential value and the complex-structure coordinates, while the vanilla six flux parameters give better predictions for the axio-dilaton coordinates under the tested settings.
Related
BibTeX
@misc{hamada2026data,
title = {{Data analysis of millions of Type IIB flux vacua}},
author = {Yuta Hamada and Yuma Uematsu and Masahito Yamazaki},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.30273},
eprint = {2610.30273},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.30273},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Data analysis of millions of Type IIB flux vacua
AU - Yuta Hamada
AU - Yuma Uematsu
AU - Masahito Yamazaki
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.30273
UR - https://arxiv.org/abs/2610.30273
AB - We study the vacuum structure of flux compactifications of type IIB string theory on the degree 18 hypersurface in ℂℙ^4_1,1,1,6,9 with two effective complex structure moduli. We scan 30,770,178 flux choices compatible with the tadpole bound, and use a gradient-based constrained minimization based on the SLSQP algorithm to identify candidates for local minima of the scalar potential, yielding a dataset of millions of candidate flux vacua. Using the 1,296,633 reliable solutions selected from this dataset, we analyze distributions and correlations of various physical quantities such as vacuum expectation values of moduli and vacuum energy. Using quadratic flux invariants as input features, we train a classifier to identify solutions satisfying the numerical SUSY criterion |V|<10^−4.5. On a randomly selected test set, selecting the top-ranked 5% recovers 47.2% of the SUSY test solutions. We also use supervised regression to predict the potential and moduli field values. In the regression comparison, quadratic flux invariant inputs improve prediction of potential value and the complex-structure coordinates, while the vanilla six flux parameters give better predictions for the axio-dilaton coordinates under the tested settings.
ER -