Extensive search for bias in SNIa data
  The use of advanced statistical analysis tools is crucial in order to improve
cosmological parameter estimates via removal of systematic errors and
identification of previously unaccounted for cosmological signals. Here we
demonstrate the application of a new fully-Bayesian method, the internal
robustness formalism, to scan for systematics and new signals in the recent
supernova Ia Union compilations. Our analysis is tailored to maximize chances
of detecting the anomalous subsets by means of a variety of sorting algorithms.
We analyse supernova Ia distance moduli for effects depending on angular
separation, redshift, surveys and hemispherical directions. The data have
proven to be robust within 2 sigma, giving an independent confirmation of
successful removal of systematics-contaminated supernovae. Hints of new
cosmology, as for example the anisotropies reported by Planck, do not seem to
be reflected in the supernova Ia data.
