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 analyzed 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%) 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.
