Metastable vacua from torsion and machine learning

TL;DR

This paper uses machine learning to identify conditions for constructing stable Anti de Sitter and de Sitter vacua in string theory compactifications, revealing over 170 stable dS vacua with potential instability issues.

Contribution

It introduces an error-function-based machine learning approach to find minimal conditions for stable vacua in type IIB string theory with torsion, including uplift mechanisms.

Findings

Over 170 stable dS vacua identified

Uplifted potentials are very flat, indicating possible instabilities

Conditions restrict configurations with certain orientifold and brane counts

Abstract

By implementing an error function on a Machine Learning algorithm we look for minimal conditions to construct stable Anti de Sitter and de Sitter vacua from dimensional type IIB String theory compactifcation on K\"ahler manifolds with torsion. This allows to have contributions to the scalar potential from the five-form flux and from D-branes wrapping torsional cycles, interpreted as non-BPS states. The former implies the possibility to construct stable AdS vacua while the later constitutes a mechanism to uplift AdS to dS vacua. Particularly we consider $\hat{D5}$ non-BPS states to uplift the stable AdS vacua to an (apparently) stable dS minimum. Both results -- the generation of an AdS vacuum and the corresponding uplifting to a dS one -- are restricted to certain type of configurations, specifically with the number of O3 orientifolds bounded from below by the number of D3-branes and fluxes. Under these conditions we report over 170 dS (classical) stable vacua. In all of them, the uplifted effective potential becomes very flat indicating the presence of possible source of instabilities. We comment about their relation with the Swampland Conjectures.