Professor Steve Abel s.a.abel@durham.ac.uk
Professor
The fate of shift-symmetries in effective string models is considered beyond tree-level. Such symmetries have been proposed in the past as a way to maintain a hierarchically small Higgs mass and also play a role in schemes of cosmological relaxation. It is argued that on general grounds one expects shift-symmetries to be restored in the limit of certain asymmetric compactifications, to all orders in perturbation theory. This behaviour is verified by explicit computation of the Kähler potential to one-loop order.
Abel, S., & Stewart, R. (2016). Shift-Symmetries at Higher Order. Journal of High Energy Physics, 2016(2), Article 182. https://doi.org/10.1007/jhep02%282016%29182
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 8, 2016 |
Online Publication Date | Feb 29, 2016 |
Publication Date | Feb 29, 2016 |
Deposit Date | Dec 10, 2015 |
Publicly Available Date | Mar 8, 2016 |
Journal | Journal of High Energy Physics |
Print ISSN | 1126-6708 |
Electronic ISSN | 1029-8479 |
Publisher | Scuola Internazionale Superiore di Studi Avanzati (SISSA) |
Peer Reviewed | Peer Reviewed |
Volume | 2016 |
Issue | 2 |
Article Number | 182 |
DOI | https://doi.org/10.1007/jhep02%282016%29182 |
Public URL | https://durham-repository.worktribe.com/output/1396458 |
Published Journal Article
(443 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Open Access. © The Authors. Article funded by SCOAP3. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
Quantum optimization of complex systems with a quantum annealer
(2022)
Journal Article
Cosmic Inflation and Genetic Algorithms
(2022)
Journal Article
Ising Machines for Diophantine Problems in Physics
(2022)
Journal Article
Completely quantum neural networks
(2022)
Journal Article
Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning
(2022)
Journal Article
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search