Concordance and Discordance in Cosmology

TL;DR

This paper introduces a comprehensive set of estimators to detect and analyze tensions and discrepancies in cosmological data, aiding in identifying systematic effects or new physics within the standard LCDM model.

Contribution

It develops versatile estimators for assessing agreement and disagreement in cosmological data, accounting for prior information and statistical significance, applicable across various models and data complexities.

Findings

Discrepancies within the LCDM model exceed 95% probability threshold.

Estimators effectively identify tensions in diverse cosmological probes.

Several tensions warrant further investigation to understand their origins.

Abstract

The success of present and future cosmological studies is tied to the ability to detect discrepancies in complex data sets within the framework of a cosmological model. Tensions caused by the presence of unknown systematic effects need to be isolated and corrected to increase the overall accuracy of parameter constraints, while discrepancies due to new physical phenomena need to be promptly identified. We develop a full set of estimators of internal and mutual agreement and disagreement, whose strengths complement each other. These allow to take into account the effect of prior information and compute the statistical significance of both tensions and confirmatory biases. We apply them to a wide range of state of the art cosmological probes and show that these estimators can be easily used, regardless of model and data complexity. We derive a series of results that show that discrepancies indeed arise within the standard LCDM model. Several of them exceed the probability threshold of 95% and deserve a dedicated effort to understand their origin.