A control variate technique using differenced estimates from realistic masked and isotropic simulations reduces the computational cost of CMB lensing bias calculations by a factor of three to five.
Pseudo-$C_\ell$ estimators which do not mix E and B modes
2 Pith papers cite this work. Polarity classification is still indexing.
abstract
Pseudo-$C_\ell$ quadratic estimators for CMB temperature and polarization power spectra have been used in the analysis pipelines of many CMB experiments, such as WMAP and Boomerang. In the polarization case, these estimators mix E and B modes, in the sense that the estimated B-mode power is nonzero for a noiseless CMB realization which contains only E-modes. Recently, Challinor & Chon showed that for moderately sized surveys ($f_{sky} \sim 0.01$), this mixing limits the gravity wave B-mode signal which can be detected using pseudo-$C_\ell$ estimators to $T/S \sim 0.05$. We modify the pseudo-$C_\ell$ construction, defining ``pure'' pseudo-$C_\ell$ estimators, which do not mix E and B modes in this sense. We study these estimators in detail for a survey geometry similar to that which has been proposed for the QUIET experiment, for a variety of noise levels, and both homogeneous and inhomogeneous noise. For noise levels $\simle 20$ $\mu$K-arcmin, our modification significantly improves the B-mode power spectrum errors obtained using pseudo-$C_\ell$ estimators. In the homogeneous case, we compute optimal power spectrum errors using a Fisher matrix approach, and show that our pure pseudo-$C_\ell$ estimators are roughly 80% of optimal, across a wide range of noise levels. There is no limit, imposed by the estimators alone, to the value of $T/S$ which can be detected.
citation-role summary
citation-polarity summary
fields
astro-ph.CO 2years
2026 2verdicts
UNVERDICTED 2roles
method 1polarities
use method 1representative citing papers
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.
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Faster CMB lensing with control variates
A control variate technique using differenced estimates from realistic masked and isotropic simulations reduces the computational cost of CMB lensing bias calculations by a factor of three to five.
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BROOM: a python package for model-independent analysis of microwave astronomical data
BROOM is a Python package that applies ILC and GILC techniques for model-independent separation of CMB, SZ, and foreground signals in microwave data along with diagnostic and simulation utilities.