Consistency tests in cosmology using relative entropy

Andrina Nicola, Adam Amara, Alexandre Refregier

Research output: Contribution to journalArticlepeer-review


With the high-precision data from current and upcoming experiments, it becomes increasingly important to perform consistency tests of the standard cosmological model. In this work, we focus on consistency measures between different data sets and methods that allow us to assess the goodness of fit of different models. We address both of these questions using the relative entropy or Kullback-Leibler (KL) divergence [1]. First, we revisit the relative entropy as a consistency measure between data sets and further investigate some of its key properties, such as asymmetry and path dependence. We then introduce a novel model rejection framework, which is based on the relative entropy and the posterior predictive distribution. We validate the method on several toy models and apply it to Type Ia supernovae data from the JLA and CMB constraints from Planck 2015, testing the consistency of the data with six different cosmological models.

Original languageEnglish
Article number011
JournalJournal of Cosmology and Astroparticle Physics
Issue number1
Publication statusPublished - 3 Jan 2019


  • Cosmological parameters from CMBR
  • Supernova type Ia-standard candles


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