Academic Thesis

Basic information

Name Yamauchi Daisuke
Belonging department Physics
Occupation name
researchmap researcher code B000327615
researchmap agency Okayama University of Science

Title

Observational signatures of dark energy produced in an ancestor vacuum : Forecast for galaxy surveys

Bibliography Type

Joint Author

Author

Daisuke Yamauchi, Hajime Aoki, Satoshi Iso, Da-Shin Lee, Yasuhiro Sekino, Chen-Pin Yeh

Summary

We study observational consequences of the model for dark energy proposed in [1]. We assume our universe has been created by bubble nucleation, and consider quantum fluctuations of an ultralight scalar field. Residual effects of fluctuations generated in an ancestor vacuum (de Sitter space in which the bubble was formed) is interpreted as dark energy. Its equation of state parameter wDE(z) has a characteristic form, approaching −1 in the future, but −1/3 in the past. A novel feature of our model is that dark energy effectively increases the magnitude of the negative spatial curvature in the evolution of the Hubble parameter, though it does not alter the definition of the angular diameter distance. We perform Fisher analysis to forecast the constraints for our model from future galaxy surveys by Square Kilometre Array and Euclid, and point out that our model can be distinguished from the usual ΛCDM model for reasonable choices of the parameters. Due to degeneracy between dark energy and the spatial curvature, it might be difficult to fully determine the model parameters by galaxy surveys alone, but combination with other independent observations, such as CMB, will greatly improve the chance of determining them.

Magazine(name)

Journal of Cosmology and Astroparticle Physics

Publisher

Volume

05

Number Of Pages

2019

StartingPage

055

EndingPage

Date of Issue

2019/05

Referee

Exist

Invited

Not exist

Language

English

Thesis Type

Research papers (academic journals)

ISSN

DOI

10.1088/1475-7516/2019/05/055

NAID

PMID

URL

J-GLOBAL ID

arXiv ID

1807.07904

ORCID Put Code

DBLP ID