Journal article icon

Journal article

Calabi–Yau metrics with Kähler moduli dependence

Abstract:
Abstract We present a method to construct approximate analytic expressions for Ricci-flat K"ahler metrics on Calabi–-Yau threefolds with explicit dependence on the K"ahler moduli. Our strategy combines numerical data obtained from machine learning with an explicit analytic Ansatz for the K"ahler potential and symbolic regression methods. Specifically, we use neural networks to learn the K"ahler potential at selected points in K"ahler moduli space, fit this data to analytic expressions with K"ahler moduli-dependent parameters, and determine an analytic form of these coefficients as functions of the K"ahler moduli using symbolic regression. In this way, we reconstruct closed-form approximations to the Ricci-flat metric that retain explicit K"ahler-moduli dependence. We apply this method to two Calabi–-Yau threefolds with $h^{1,1}=2$, namely a bicubic hypersurface in $\mathbb{P}^2 \times \mathbb{P}^2$ and a bi-degree $(2,4)$ hypersurface in $\mathbb{P}^1 \times \mathbb{P}^3$, both of which admit nontrivial discrete symmetry groups that simplify the structure of the metric. In both cases, the resulting analytic expressions reproduce the numerically learned K"ahler potentials with percent-level accuracy and yield a Ricci-flatness measure that remains sufficiently small across the sampled region. Our results represent a concrete bridge between purely numerical results for Calabi–-Yau metrics and analytic constructions, opening the door to a systematic study of their dependence on K"ahler moduli.
Publication status:
Published
Peer review status:
Peer reviewed

Actions

Access Document

Files:
Publisher copy:
10.1088/2632-2153/ae8400

Authors

More by this author
Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-0861-5363
More by this author
Institution:
University of Oxford
Role:
Author
More by this author
Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-5045-5113


Publisher:
IOP Publishing
Journal:
Machine Learning: Science and Technology More from this journal
Volume:
7
Issue:
4
Pages:
045020-045020
Publication date:
2026-06-29
DOI:
EISSN:
2632-2153
ISSN:
2632-2153


Language:
English
Keywords:
Pubs id:
2443789
Local pid:
pubs:2443789
Source identifiers:
W7166535435
Deposit date:
2026-07-18
ARK identifier:
This ORA record was generated from metadata provided by an external service. It has not been edited by the ORA Team.

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP