  The maximum likelihood estimator for CMB map-making is optimal and unbiased
as long as the data model is correct, but in practice it rarely is, with model
errors including sub-pixel structure and instrumental problems like
time-variable gain and pointing errors. In the presence of such errors, the
solution is biased, with the local error in each pixel leaking outwards along
the scanning pattern by a noise correlation length. The most important sources
of such leakage are strong point sources, and for common scanning patterns the
leakage manifests as an X around each such source. I discuss why this happens,
and present several old and new methods for mitigating and/or eliminating this
leakage, along with a small stand-alone TOD simulator and map-maker in Python
that implements them.
