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Searching for bias and correlations in a Bayesian way - Example: SN Ia data

Published online by Cambridge University Press:  01 July 2015

Caroline Heneka ,
Alexandre Posada ,
Valerio Marra  and
Luca Amendola
Caroline Heneka
Affiliation:
Dark Cosmology Centre, Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, DK-2100 Copenhagen, Denmark email: caroline@dark-cosmology.dk
Alexandre Posada
Affiliation:
Centre de Physique théorique, Université d'Aix-Marseille, Campus de Luminy Case 907, 13288 Marseille cedex 9, France
Valerio Marra
Affiliation:
Instituto de Física, Universidade Federal do Rio de Janeiro CEP 21941-972, Rio de Janeiro, RJ, Brazil
Luca Amendola
Affiliation:
Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, D-69120 Heidelberg, Germany
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Abstract

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A range of Bayesian tools has become widely used in cosmological data treatment and parameter inference (see Kunz et al.2007, Trotta 2008, Amendola et al.2013). With increasingly big datasets and higher precision, tools that enable us to further enhance the accuracy of our measurements gain importance. Here we present an approach based on internal robustness, introduced in Amendola et al. (2013) and adopted in Heneka et al. (2014), to identify biased subsets of data and hidden correlation in a model independent way.

Keywords

methods: statisticalcosmology: cosmological parametersstars: supernovae: general

References

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