City Research Online

Getting CICY high

Bull, K., He, Y. ORCID: 0000-0002-0787-8380, Jejjala, V. and Mishra, C. (2019). Getting CICY high. Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics, 795, pp. 700-706. doi: 10.1016/j.physletb.2019.06.067

Abstract

Supervised machine learning can be used to predict properties of string geometries with previously unknown features. Using the complete intersection Calabi–Yau (CICY) threefold dataset as a theoretical laboratory for this investigation, we use low h1,1 geometries for training and validate on geometries with large h1,1. Neural networks and Support Vector Machines successfully predict trends in the number of Kähler parameters of CICY threefolds. The numerical accuracy of machine learning improves upon seeding the training set with a small number of samples at higher h1,1.

Publication Type: Article
Additional Information: © 2019 The Authors. Published by Elsevier B.V.This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Publisher Keywords: Machine learning, Neural network, Support Vector Machine Calabi–Yau, String compactifications
Subjects: Q Science > QA Mathematics
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
URI: http://openaccess.city.ac.uk/id/eprint/22588
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