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More about a successful vector-tensor theory of gravitation


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Title:
More about a successful vector-tensor theory of gravitation
Authors:
Dale, Roberto  
Saéz, Diego
Editor:
IOP Publishing
Department:
Departamentos de la UMH::Estadística, Matemáticas e Informática
Issue Date:
2017-01-02
URI:
https://hdl.handle.net/11000/30694
Abstract:
The vector-tensor (VT) theory of gravitation revisited in this article was studied in previous papers, where it was proved that VT works and deserves attention. New obser vational data and numerical codes have motivated further development which is presented here. New research has been planed with the essential aim of proving that current cosmo logical observations, including Planck data, baryon acoustic oscillations (BAO), and so on, may be explained with VT, a theory which accounts for a kind of dark energy which has the same equation of state as vacuum. New versions of the codes CAMB and COSMOMC have been designed for applications to VT, and the resulting versions have been used to get the cosmological parameters of the VT model at suitable confidence levels. The parameters to be estimated are the same as in general relativity (GR), plus a new parameter D. For D = 0, VT linear cosmological perturbations reduces to those of GR, but the VT background may explain dark energy. The fits between observations and VT predictions lead to non vanishing |D| upper limits at the 1σ confidence level. The value D = 0 is admissible at this level, but this value is not that of the best fit in any case. Results strongly suggest that VT may explain current observations, at least, as well as GR; with the advantage that, as it is proved in this paper, VT has an additional parameter which facilitates adjustments to current observational data.
Keywords/Subjects:
cosmological parameters from CMBR
modified gravity
dark energy theory
Knowledge area:
CDU: Generalidades.: Ciencia y tecnología de los ordenadores. Informática.
Type of document:
application/pdf
Access rights:
info:eu-repo/semantics/closedAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
DOI:
https://doi.org/10.1088/1475-7516/2017/01/004
Appears in Collections:
Artículos Estadística, Matemáticas e Informática



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