We use ALMA observations of CO(2–1) in 13 massive (
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi–Yau manifolds using deep learning. In this paper, we consider the dataset of all Calabi–Yau fourfolds constructed as complete intersections in products of projective spaces. Employing neural networks inspired by stateoftheart computer vision architectures, we improve earlier benchmarks and demonstrate that all four nontrivial Hodge numbers can be learned at the same time using a multitask architecture. With 30% (80%) training ratio, we reach an accuracy of 100% for
 Award ID(s):
 2019786
 Publication Date:
 NSFPAR ID:
 10303335
 Journal Name:
 Machine Learning: Science and Technology
 Volume:
 3
 Issue:
 1
 Page Range or eLocationID:
 Article No. 015006
 ISSN:
 26322153
 Publisher:
 IOP Publishing
 Sponsoring Org:
 National Science Foundation
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