SILC: a new Planck Internal Linear Combination CMB temperature map using directional wavelets

TL;DR

SILC introduces a novel directional wavelet-based internal linear combination method to produce cleaner, more localized CMB temperature maps from Planck data, enhancing foreground separation by leveraging morphological information.

Contribution

The paper presents SILC, a new component separation algorithm using directional wavelets that improves localization of foreground removal in CMB maps compared to previous methods.

Findings

SILC produces CMB maps consistent with existing algorithms.

Directional wavelets improve foreground localization.

Combining directional and axisymmetric wavelets enhances map quality.

Abstract

We present new clean maps of the CMB temperature anisotropies (as measured by Planck) constructed with a novel internal linear combination (ILC) algorithm using directional, scale-discretised wavelets --- Scale-discretised, directional wavelet ILC or SILC. Directional wavelets, when convolved with signals on the sphere, can separate the anisotropic filamentary structures which are characteristic of both the CMB and foregrounds. Extending previous component separation methods, which use the frequency, spatial and harmonic signatures of foregrounds to separate them from the cosmological background signal, SILC can additionally use morphological information in the foregrounds and CMB to better localise the cleaning algorithm. We test the method on Planck data and simulations, demonstrating consistency with existing component separation algorithms, and discuss how to optimise the use of morphological information by varying the number of directional wavelets as a function of spatial scale. We find that combining the use of directional and axisymmetric wavelets depending on scale could yield higher quality CMB temperature maps. Our results set the stage for the application of SILC to polarisation anisotropies through an extension to spin wavelets.