More about a successful vector-tensor theory of gravitation

TL;DR

This paper revisits the vector-tensor theory of gravitation, demonstrating its potential to explain cosmological observations, including dark energy, with an additional parameter improving data fit compared to general relativity.

Contribution

The paper develops new numerical codes for vector-tensor gravity, fitting cosmological data to show VT's viability and its advantage of an extra parameter for better data alignment.

Findings

VT can explain dark energy with a non-zero parameter D.

Current data do not exclude VT as an alternative to GR.

VT fits observational data as well as GR, with an extra parameter.

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 observational 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 cosmological 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 ingeneral 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 $1\sigma $ 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.}