Kavli Affiliate: Anthony Challinor
| First 5 Authors: Louis Legrand, Louis Legrand, , ,
| Summary:
Noise maps from CMB experiments are generally statistically anisotropic, due
to scanning strategies, atmospheric conditions, or instrumental effects. Any
mis-modeling of this complex noise can bias the reconstruction of the lensing
potential and the measurement of the lensing power spectrum from the observed
CMB maps. We introduce a new CMB lensing estimator based on the maximum a
posteriori (MAP) reconstruction that is minimally sensitive to these
instrumental noise biases. By modifying the likelihood to rely exclusively on
correlations between CMB map splits with independent noise realizations, we
minimize auto-correlations that contribute to biases. In the regime of many
independent splits, this maximum closely approximates the optimal MAP
reconstruction of the lensing potential. In simulations, we demonstrate that
this method is able to determine lensing observables that are immune to any
noise mis-modeling with a negligible cost in signal-to-noise ratio. Our
estimator enables unbiased and nearly optimal lensing reconstruction for
next-generation CMB surveys.
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