| Article | |
| Report number | arXiv:2012.04656 ; CERN-TH-2020-205 |
| Title | Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning |
| Related title | Moduli-dependent Calabi-Yau and $SU$(3)-structure metrics from Machine Learning |
| Author(s) | Anderson, Lara B. (Virginia Tech.) ; Gerdes, Mathis (Munich U., ASC) ; Gray, James (Virginia Tech.) ; Krippendorf, Sven (Munich U., ASC) ; Raghuram, Nikhil (Virginia Tech.) ; Ruehle, Fabian (CERN ; Oxford U., Theor. Phys.) |
| Publication | 2021-05-03 |
| Imprint | 2020-12-08 |
| Number of pages | 15 |
| Note | minor changes, 27+15 pages, 12 figures, 3 tables |
| In: | JHEP 2105 (2021) 013 |
| DOI | 10.1007/JHEP05(2021)013 |
| Subject category | hep-th ; Particle Physics - Theory |
| Abstract | We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve existing numerical approximations in terms of accuracy and speed. Knowing these metrics has numerous applications, ranging from computations of crucial aspects of the effective field theory of string compactifications such as the canonical normalizations for Yukawa couplings, and the massive string spectrum which plays a crucial role in swampland conjectures, to mirror symmetry and the SYZ conjecture. In the case of SU(3) structure, our machine learning approach allows us to engineer metrics with certain torsion properties. Our methods are demonstrated for Calabi-Yau and SU(3)-structure manifolds based on a one-parameter family of quintic hypersurfaces in $\mathbb{P}^4.$ |
| Copyright/License | preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2021-2025 The Authors (License: CC-BY-4.0), sponsored by SCOAP³ |
Corresponding record in: Inspire
Notice créée le 2020-12-11, modifiée le 2023-11-16
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