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We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to checktheir consistency using cosmology blind statistical analyses as well ascosmological parameter fitting. We find that Panstarrs dataset is the moststable of the three to changes, although it does not, at the moment, go to highenough redshifts to tightly constrain the dark energy equation of state, $w$.Union2.1, drawn from many different sources, appears somewhat susceptible tochanges within the dataset. JLA reconstructs well for a smaller number ofcosmological parameters. At higher degrees of freedom, the dependence of itserrors on redshift can lead to varying results between subsets. Panstarrs isinconsistent with the other two at about $2\sigma$, and JLA and Union2.1 areabout $1\sigma$ away from each other. For the $\Omega_{0m}-w$ cosmologicalreconstruction, the $1\sigma$ range of values in $w$ for selected subsets ofeach 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 similarfor all three datasets. Although there are differences in the fitting andcorrection techniques used in the different samples, the most importantcriterion is SNe selection, a slightly different SNe selection can lead tonoticeably different results both in the purely statistical analysis andcosmological reconstruction. We note that a single, high quality low redshiftsample could help decrease the uncertainties in the result. We also note thatlack of homogeneity in the magnitude errors may bias the results and shouldeither be modeled, or its effect neutralized by using other, complementarydatasets. A supernova sample with high quality data at both high and lowredshifts, constructed from a few surveys to avoid heterogeneity in the sample,and with homogeneous errors, would result in a more robust cosmologicalreconstruction.

An Exploration of Heterogeneity in Supernova Type Ia Samples