Skip to Main content Skip to Navigation
Conference papers

Independent Component Separation from incomplete spherical data using wavelets. Application to CMB data analysis

Abstract : Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space that was designed to address in a flexible way some of the general problems raised by Cosmic Microwave Background data analysis. However, a common issue in astronomical data analysis is that the observations are unevenly sampled or incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are not well modeled as stationary processes over the sky. These effects impair data processing techniques in the spherical harmonics representation. This paper describes a new wavelet transform for spherical maps and proposes an extension of SMICA in this space-scale representation.
Complete list of metadatas

http://hal.in2p3.fr/in2p3-00122886
Contributor : Dominique Girod <>
Submitted on : Friday, January 5, 2007 - 1:57:15 PM
Last modification on : Saturday, September 19, 2020 - 4:38:12 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 7:55:53 PM

Identifiers

  • HAL Id : in2p3-00122886, version 1

Citation

Y. Moudden, P. Abrial, P. Vielva, J.-B. Melin, Jean-Luc Starck, et al.. Independent Component Separation from incomplete spherical data using wavelets. Application to CMB data analysis. PSIP'2005 : Physics in signal and Image processing, Jan 2005, Toulouse, France. ⟨in2p3-00122886⟩

Share

Metrics

Record views

919

Files downloads

736