Needlet thresholding methods in component separation

TL;DR

This paper introduces needlet thresholding algorithms to improve component separation in CMB data, demonstrating enhanced denoising, data compression, and accuracy, especially in low signal-to-noise scenarios and for B-mode spectra.

Contribution

It develops novel needlet-thresholding schemes integrated with ILC and template-fitting methods for improved CMB component separation.

Findings

Thresholding improves denoising of internal templates in low SNR conditions.

The method outperforms other denoising techniques in reconstruction accuracy.

Best results are achieved with template-fitting in LSPE-like experiments, especially for B-mode spectra.

Abstract

Foreground components in the Cosmic Microwave Background (CMB) are sparse in a needlet representation, due to their specific morphological features (anisotropy, non-Gaussianity). This leads to the possibility of applying needlet thresholding procedures as a component separation tool. In this work, we develop algorithms based on different needlet-thresholding schemes and use them as extensions of existing, well-known component separation techniques, namely ILC and template-fitting. We test soft- and hard-thresholding schemes, using different procedures to set the optimal threshold level. We find that thresholding can be useful as a denoising tool for internal templates in experiments with few frequency channels, in conditions of low signal-to-noise. We also compare our method with other denoising techniques, showing that thresholding achieves the best performance in terms of reconstruction accuracy and data compression while preserving the map resolution. The best results in our tests are in particular obtained when considering template-fitting in an LSPE like experiment, especially for B-mode spectra.