Parameter compression in the flux landscape

TL;DR

This paper uses advanced data analysis techniques to explore the structure of flux vacua in string theory, revealing low-dimensional representations and topological features that could aid in model development.

Contribution

It introduces a physics-informed autoencoder and topological data analysis to uncover low-dimensional structures and correlations in flux vacua data.

Findings

Flux space effectively reduces from 12D to lower dimensions.

Autoencoder reveals meaningful latent organization of vacua.

Persistent homology uncovers robust topological features.

Abstract

We present a data-driven investigation of the exhaustive ensemble of no-scale type IIB flux vacua constructed in \cite{Chauhan:2025rdj}. Using a combination of linear and non-linear dimensionality-reduction techniques, we analyse both flux and moduli spaces and demonstrate that the effective dimensionality of the underlying 12-dimensional flux space is substantially reduced. A central component of our study is a physics-informed autoencoder, which provides a non-linear compression of the flux and moduli data into a low-dimensional latent space. The learned latent representation organises vacua according to desired features and, in particular, isolates distinguished regions associated with small values of the flux superpotential $|W_0|$, revealing non-trivial correlations that are not captured by linear methods. In parallel, we apply tools from topological data analysis, specifically persistent homology, to probe the global structure of the vacuum distribution. This allows us to identify robust, long-lived topological features in both moduli and flux subspaces. This work is a necessary step for developing foundation models in string phenomenology.