Internal robustness of growth rate data

Abstract

We perform an internal robustness analysis (iR) to a compilation of the most recent fσ8ðzÞ data, using the framework of Ref. [1]. The method analyzes combinations of subsets in the data set in a Bayesian model comparison way, potentially finding outliers, subsets of data affected by systematics or new physics. In order to validate our analysis and assess its sensitivity we performed several cross-checks, for example by removing some of the data or by adding artificially contaminated points, while we also generated mock data sets in order to estimate confidence regions of the iR. Applying this methodology, we found no anomalous behavior in the fσ8ðzÞ data set, thus validating its internal robustness.