Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

Beyond Lambda CDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming Lambda CDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of Lambda CDM. We find that the DES Y1 data have an acceptable goodness of fit to Lambda CDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

Automated summary

The study demonstrates that data from Dark Energy Survey Year 1 have a good fit to the Lambda CDM model, with most of the data vector being consistent with expectations, although there is a minor tension between large- and small-scale measurements. A small portion of the data shows a significant departure from expectations, but excluding it has minimal impact on cosmological constraints and improves the p-value significantly.