Internal Robustness: systematic search for systematic bias in SN Ia data
  A great deal of effort is currently being devoted to understanding,
estimating and removing systematic errors in cosmological data. In the
particular case of type Ia supernovae, systematics are starting to dominate the
error budget. Here we propose a Bayesian tool for carrying out a systematic
search for systematic contamination. This serves as an extension to the
standard goodness-of-fit tests and allows not only to cross-check raw or
processed data for the presence of systematics but also to pin-point the data
that are most likely contaminated. We successfully test our tool with mock
catalogues and conclude that the Union2.1 data do not possess a significant
amount of systematics. Finally, we show that if one includes in Union2.1 the
supernovae that originally failed the quality cuts, our tool signals the
presence of systematics at over 3.8-sigma confidence level.
