An Exploration of Heterogeneity in Supernova Type Ia Samples

Abstract

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that Panstarrs dataset is the most stable of the three to changes, although it does not, at the moment, go to high enough redshifts to tightly constrain the dark energy equation of state, $w$. Union2.1, drawn from many different sources, appears somewhat susceptible to changes within the dataset. JLA reconstructs well for a smaller number of cosmological parameters. At higher degrees of freedom, the dependence of its errors on redshift can lead to varying results between subsets. Panstarrs is inconsistent with the other two at about $2\sigma$, and JLA and Union2.1 are about $1\sigma$ away from each other. For the ${\Omega }_{0m}-w$ cosmological reconstruction, the $1\sigma$ range of values in $w$ for selected subsets of each dataset is two times larger for JLA and Union2.1 as compared to Panstarrs. The range in ${\Omega }_{0m}$ for the same subsets remains approximately similar for all three datasets. Although there are differences in the fitting and correction techniques used in the different samples, the most important criterion is SNe selection, a slightly different SNe selection can lead to noticeably different results both in the purely statistical analysis and cosmological reconstruction. We note that a single, high quality low redshift sample could help decrease the uncertainties in the result. We also note that lack of homogeneity in the magnitude errors may bias the results and should either be modeled, or its effect neutralized by using other, complementary datasets. A supernova sample with high quality data at both high and low redshifts, constructed from a few surveys to avoid heterogeneity in the sample, and with homogeneous errors, would result in a more robust cosmological reconstruction.

32 pages, 17 figures. Accepted for publication in JCAP

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