A hybrid approach to CMB lensing reconstruction on all-sky intensity maps

TL;DR

This paper introduces a hybrid method for reconstructing the CMB lensing potential power spectrum on all-sky intensity maps, combining full-sky inpainting for low multipoles and a multi-patch approach for higher multipoles, effectively reducing bias and noise.

Contribution

It presents a novel hybrid reconstruction technique that handles inhomogeneous noise and galactic masks, improving accuracy in CMB lensing analysis on all-sky maps.

Findings

Multi-patch approach reduces bias and noise in lensing spectrum reconstruction.

Full-sky inpainting effective for low multipoles with minimal variance.

New fast Fourier coefficient determination method for irregular data.

Abstract

Based on realistic simulations, we propose an hybrid method to reconstruct the lensing potential power spectrum, directly on PLANCK-like CMB frequency maps. It implies using a large galactic mask and dealing with a strong inhomogeneous noise. For l < 100, we show that a full-sky inpainting method, already described in a previous work, still allows a minimal variance reconstruction, with a bias that must be accounted for by a Monte-Carlo method, but that does not couple to the deflection field. For l>100 we develop a method based on tiling the cut-sky with local 10x10 degrees overlapping tangent planes (referred to in the following as "patches"). It requires to solve various issues concerning their size/position, non-periodic boundaries and irregularly sampled data after the sphere-to-plane projection. We show how the leading noise term of the quadratic lensing estimator applied onto an apodized patch can still be taken directly from the data. To not loose spatial accuracy, we developed a tool that allows the fast determination of the complex Fourier series coefficients from a bi-dimensional irregularly sampled dataset, without performing an interpolation. We show that the multi-patch approach allows the lensing power spectrum reconstruction with a very small bias, thanks to avoiding the galactic mask and lowering the noise inhomogeneities, while still having almost a minimal variance. The data quality can be assessed at each stage and simple bi-dimensional spectra build, which allows the control of local systematic errors.