Distance Measurements from Supernovae and Dark Energy Constraints
Yun Wang

TL;DR
This paper investigates how flux-averaging of Type Ia supernova data affects dark energy constraints, demonstrating that it reduces systematic uncertainties and leads to more robust measurements when combined with other cosmological data.
Contribution
The study introduces flux-averaging as a method to test systematic uncertainties in supernova data and applies it to improve dark energy measurements from multiple datasets.
Findings
Flux-averaging reduces systematic uncertainties in SN Ia distance measurements.
Combined data with flux-averaging show consistency with a cosmological constant.
Dark energy is detected at >98% confidence level for z<= 0.75 with the combined dataset.
Abstract
Constraints on dark energy from current observational data are sensitive to how distances are measured from Type Ia supernova (SN Ia) data. We find that flux-averaging of SNe Ia can be used to test the presence of unknown systematic uncertainties, and yield more robust distance measurements from SNe Ia. We have applied this approach to the nearby + SDSS +ESSENCE +SNLS +HST set of 288 SNe Ia, and the ``Constitution''of set 397 SNe Ia. Combining the SN Ia data with CMB data from WMAP five year observations, the Sloan Digital Sky Survey baryon acoustic oscillation measurements, the data of 69 gammay-ray bursts, and the Hubble constant measurement from the HST project SHOES, we measure the dark energy density function X(z)=\rho_X(z)/\rho_X(0) as a free function of redshift (assumed to be a constant at z>1 or z>1.5). Without flux-averaging of SNe Ia, the combined data using the…
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