  The identification of unsubtracted foreground residuals in the cosmic
microwave background maps on large scales is of crucial importance for the
analysis of polarization signals. These residuals add a non-Gaussian
contribution to the data. We propose the Kullback-Leibler (KL) divergence as an
effective, non-parametric test on the one-point probability distribution
function of the data. With motivation in information theory, the KL divergence
takes into account the entire range of the distribution and is highly
non-local. We demonstrate its use by analyzing the large scales of the Planck
2013 SMICA temperature fluctuation map and find it consistent with the expected
distribution at a level of 6%. Comparing the results to those obtained using
the more popular Kolmogorov-Smirnov test, we find the two methods to be in
general agreement.
