Estimating the redshift error in supernova data analysis

Abstract

Recent works have shown that small shifts in redshift -- gravitational redshift or systematic errors -- could potentially cause a significant bias in the estimation of cosmological parameters. I aim to verify whether a theoretical correction on redshift is sufficient to ease the tension between the estimates of cosmological parameters from SNe 1a dataset and Planck 2015 results. A free parameter for redshift shift($\Delta z$) is implemented in the Maximum Likelihood Estimator. Redshift error was estimated from the Joint Light-curve Analysis(JLA) dataset and results from the Planck 2015 survey. The estimation from JLA dataset alone gives a best fit value of ${\Omega }_m=0.272$, ${\Omega }_{\Lambda }=0.390$, and $\Delta z=3.77\times 10^{-4}$. The best fit values of both ${\Omega }_m$ and ${\Omega }_{\Lambda }$ disagrees heavily with results from other observations. Information criteria and observed density contrasts suggest that the current data from SNe 1a is not accurate enough to give a proper estimate of $\Delta z$. A joint analysis with Planck results seems to give a more plausible value of the redshift error, and can potentially be used as a probe to measure our local gravitational environment.

arXiv comment7 pages

References in corpus (7)

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• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• Gravitational redshift of galaxies in clusters as predicted by general relativity
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