Skip to main content

Blind Component Separation in Wavelet Space: Application to CMB Analysis

Abstract

It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA) is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Y. Moudden.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Moudden, Y., Cardoso, JF., Starck, JL. et al. Blind Component Separation in Wavelet Space: Application to CMB Analysis. EURASIP J. Adv. Signal Process. 2005, 484606 (2005). https://doi.org/10.1155/ASP.2005.2437

Download citation

Keywords and phrases

  • blind source separation
  • cosmic microwave background
  • wavelets
  • data analysis
  • missing data