An Exploration of Heterogeneity in Supernova Type Ia Samples
  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.
