A Comparative Study of Dark Energy Constraints from Current Observational Data
Yun Wang, Chia-Hsun Chuang, and Pia Mukherjee

TL;DR
This study investigates how different analysis methods, including flux-averaging of supernova data and galaxy clustering measurements, affect dark energy constraints, finding that combined data support a cosmological constant and flat universe.
Contribution
It generalizes the flux-averaging method for SNe Ia to include correlated errors, demonstrating its impact on dark energy constraints when combined with other observational data.
Findings
Flux-averaging increases errors when used alone.
Combined data with flux-averaging tighten dark energy constraints.
Results are consistent with a cosmological constant and flat universe.
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 flux-averaging 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 flux-averaging 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 year 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, flux-averaging of SNe Ia increases the concordance with other data, and leads to significantly tighter…
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