Not So Flat Metrics
Abstract
In order to be in control of the derivative expansion, geometric string compactifications are understood in the context of a large volume approximation. In this letter, we consider the reduction of these higher derivative terms, and propose an improved estimate on the large volume approximation using numerical Calabi-Yau metrics obtained via machine learning methods. Further to this, we consider the corrections to numerical Calabi-Yau metrics in the context of IIB string theory. This correction represents one of several important contributions for realistic string compactifications -- alongside, for example, the backreaction of fluxes and local sources -- all of which have important consequences for string phenomenology. As a simple application of the corrected metric, we compute the change to the spectrum of the scalar Laplacian.
Cite
@article{arxiv.2411.00962,
title = {Not So Flat Metrics},
author = {Kit Fraser-Taliente and Thomas R. Harvey and Manki Kim},
journal= {arXiv preprint arXiv:2411.00962},
year = {2025}
}