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Surprising results from a Bayesian analysis of Supernova Type Ia data

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If you have a question about this talk, please contact Daisy Mak.

Supernovae type Ia (SNIa) are one of the observational pillars of the LambdaCDM model, and have been instrumental in determining the existence of dark energy. While the observational effort has been very successful in finding over 1,000 cosmologically useful SNIa’s (and more progress is being made e.g. by extending observations to the near infrared), the sophistication of statistical methods employed to analyse the data and infer cosmological parameters has been lagging behind.

In this talk I will present new results from BAHAMAS (BAyesian HierArchical Modeling for the Analysis of Supernova cosmology), a fully Bayesian analysis of the JLA SN Ia data, a demonstrably superior approach which improves on many shortcomings of the usual method. I will report on surprising and as of yet unexplained systematic shifts in the recovered cosmological parameters: the matter content of the Universe and the curvature parameter show 2-sigma discrepancies with respect to standard cosmology. I will discuss the possibility of undetected systematic errors in the colour correction parameters. I will present some preliminary findings about BAHAMAS constraints on the anisotropy of cosmic expansion, and on the usefulness of exploiting the location of the SNIa in its host galaxy to improve the colour correction and SNIa standardization.

This talk is part of the Cosmology Lunch series.

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