Internal Robustness of Growth Rate data

TL;DR

This paper applies an internal robustness analysis to recent $f\sigma_8(z)$ data, validating its consistency and lack of anomalies through Bayesian model comparison and various cross-checks.

Contribution

It introduces a Bayesian internal robustness method to assess the consistency of growth rate data and validates its effectiveness with real and mock data.

Findings

No anomalies found in the data set

Data set is internally robust

Method can detect outliers or systematics

Abstract

We perform an Internal Robustness analysis (iR) to a compilation of the most recent $f\sigma_8(z)$ data, using the framework of 1209.1897. 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 $f\sigma_8(z)$ data set, thus validating its internal robustness.