REVIEW 2 cited by
Bias-Hardened CMB Lensing with Polarization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Polarization data will soon provide the best avenue for measurements of the CMB lensing potential, although it is potentially sensitive to several instrumental effects including beam asymmetry, polarization angle uncertainties, sky coverage, as well as analysis choices such as masking. We derive "bias-hardened" lensing estimators to mitigate these effects, at the expense of somewhat larger reconstruction noise, and test them numerically on simulated data. We find that the mean-field bias from masking is significant for the EE quadratic lensing estimator, however the bias-hardened estimator combined with filtering techniques can mitigate the mean field. On the other hand, the EB estimator does not significantly suffer from the mean-field from the point source masking and survey window function. The contamination from beam asymmetry and polarization angle uncertainties, however, can generate mean-field biases for the EB estimator. These can also be mitigated using bias-hardened estimators, with at most a factor of ~ 3 degradation of noise level compared to the conventional approach.
Forward citations
Cited by 2 Pith papers
-
Bias hardened estimators of patchy screening profiles
The authors derive and validate lensing-hardened estimators for stacked patchy screening measurements, show the unmitigated lensing bias dominates the ACT x unWISE signal, and place an upper bound on the screening amplitude.
-
Bias to CMB lensing from lensed foregrounds
Lensed extragalactic foregrounds create a percent-level bias in CMB lensing estimators that is significant for upcoming Simons Observatory measurements and can be reduced by modified estimators.
Discussion (0). Continue with ORCID to comment.