Year: 2021
Type: preprint
Abstract: We investigate reinforcement learning and genetic algorithms in the context of heterotic Calabi-Yau models with monad bundles. Both methods are found to be highly efficient in identifying phenomenologically attractive three-family models, in cases where systematic scans are not feasible. For monads on the bi-cubic Calabi-Yau either method facilitates a complete search of the environment and leads to similar sets of previously unknown three-family models.
Source: arXiv (Cornell University)
Authors: Abel, Steven, Constantin, Andrei, Harvey, Thomas R., Lukas, Andre
Institutions: Durham University, University of Oxford
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Award: G4990700415
OpenAlex ID: W3214535549