Searching the landscape of flux vacua with genetic algorithms

• Published in: The journal of high energy physics Vol. 2019; no. 11; pp. 1 - 39
• Main Authors: Cole, Alex, Schachner, Andreas, Shiu, Gary
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2019; Springer Nature B.V; Springer Berlin; SpringerOpen
• Subjects: ASTRONOMY AND ASTROPHYSICS; Chromosomes; Classical and Quantum Gravitation; Elementary Particles; Flux; Flux compactifications; Genetic algorithms; Genotype & phenotype; High energy physics; Hyperspaces; Mutation; Physics; Physics and Astronomy; Quantum Field Theories; Quantum Field Theory; Quantum Physics; Random walk; Regular Article - Theoretical Physics; Relativity Theory; String Theory; Superstring Vacua; Topography; Flux compactifications; Superstring Vacua
• ISSN: 1029-8479; 1126-6708; 1127-2236; 1029-8479
• DOI: 10.1007/JHEP11(2019)045

A bstract In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider a symmetric T 6 as well as the conifold region of a Calabi-Yau hypersurface. We argue that in both cases genetic algorithms are powerful tools for finding flux vacua with interesting phenomenological properties. We also compare genetic algorithms to algorithms based on different breeding mechanisms as well as random walk approaches.