Comparative study of dark energy constraints from current observational data
Abstract
We examine how dark energy constraints from current observational data depend on the analysis methods used: the analysis of Type Ia supernovae (SNe Ia), and that of galaxy clustering data. We generalize the fluxaveraging analysis method of SNe Ia to allow correlated errors of SNe Ia, in order to reduce the systematic bias due to weak lensing of SNe Ia. We find that fluxaveraging leads to larger errors on dark energy and cosmological parameters if only SN Ia data are used. When SN Ia data (the latest compilation by the SNLS team) are combined with WMAP 7 yr results (in terms of our Gaussian fits to the probability distributions of the CMB shift parameters), the latest Hubble constant (H_{0}) measurement using the Hubble Space Telescope (HST), and gamma ray burst (GRB) data, fluxaveraging of SNe Ia increases the concordance with other data, and leads to significantly tighter constraints on the dark energy density at z=1, and the cosmic curvature Ω_{k}. The galaxy clustering measurements of H(z=0.35)r_{s}(z_{d}) and r_{s}(z_{d})/D_{A}(z=0.35) (where H(z) is the Hubble parameter, D_{A}(z) is the angular diameter distance, and r_{s}(z_{d}) is the sound horizon at the drag epoch) by Chuang & Wang (2011) are consistent with SN Ia data, given the same pirors (CMB+H_{0}+GRB), and lead to significantly improved dark energy constraints when combined. Current data are fully consistent with a cosmological constant and a flat universe.
 Publication:

Physical Review D
 Pub Date:
 January 2012
 DOI:
 10.1103/PhysRevD.85.023517
 arXiv:
 arXiv:1109.3172
 Bibcode:
 2012PhRvD..85b3517W
 Keywords:

 98.80.Es;
 98.80.k;
 98.80.Jk;
 Observational cosmology;
 Cosmology;
 Mathematical and relativistic aspects of cosmology;
 Astrophysics  Cosmology and Extragalactic Astrophysics;
 High Energy Physics  Phenomenology
 EPrint:
 11 pages, 9 figures. Slightly revised version, to appear in PRD. Supernova fluxaveraging code available at http://www.nhn.ou.edu/~wang/SNcode/