Bias-Hardened CMB Lensing
  We present new methods for lensing reconstruction from CMB temperature
fluctuations which have smaller mean-field and reconstruction noise bias
corrections than current lensing estimators, with minimal loss of
signal-to-noise. These biases are usually corrected using Monte Carlo
simulations, and to the extent that these simulations do not perfectly mimic
the underlying sky there are uncertainties in the bias corrections. The
bias-hardened estimators which we present can have reduced sensitivity to such
uncertainties, and provide a desirable cross-check on standard results. To test
our approach, we also show the results of lensing reconstruction from simulated
temperature maps given on 100 deg^2, and confirm that our approach works well
to reduce biases for a typical masked map in which 70 square masks each having
10 arcminute on a side exist, covering 2% of the simulated map, which is
similar to the masks used in the current SPT lensing analysis.
