Extensive search for systematic bias in supernova Ia data

Abstract

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 2s, 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. © 2014 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society.