European Commission logo
English English
CORDIS - EU research results
CORDIS
Content archived on 2024-05-28

String compactifications, their low energy effective field theories and applications to physics

Objective

Recent advances in string phenomenology have produced hundreds of explicit string compactifications whose low energy particle spectra correspond to the supersymmetric standard model and no chiral exotics. This project concerns the low energy effective field theory (LEEFT) of such string compactifications, which is essential in order to go beyond the particle spectra towards fully realistic string models. In particular, with the LEEFT it will be possible to address fundamental dynamical problems - like the stabilization of moduli and supersymmetric breaking, the decoupling of vector-like exotics, realistic Yukawa couplings and cosmology - in explicit models.

The project's main focus will be heterotic orbifold compactifications, which enjoy both potentially realistic properties and high computability. New techniques will be developed to compute terms in the LEEFT, and these tools will be used to derive phenomenologically important contributions to both the superpotential and Kaehler potential. The project will also quantify some of the relations between different classes of promising string compactifications, including heterotic orbifolds, heterotic Calabi-Yaus, heterotic free-fermionic formulation, and F-theory. Finally, it will use the knowledge developed to attack the long-standing fundamental problems of moduli stabilization, decoupling of exotics and dark energy in explicit string constructions.

Call for proposal

FP7-PEOPLE-2013-IEF
See other projects for this call

Coordinator

THE UNIVERSITY OF LIVERPOOL
EU contribution
€ 309 235,20
Address
BROWNLOW HILL 765 FOUNDATION BUILDING
L69 7ZX Liverpool
United Kingdom

See on map

Region
North West (England) Merseyside Liverpool
Activity type
Higher or Secondary Education Establishments
Administrative Contact
Suzanne Halpin (Ms.)
Links
Total cost
No data