All-sky astrophysical component separation with Fast Independent Component Analysis (FastICA)

By D. Maino, A. Farusi, C. Baccigalupi, Francesco Perrotta, A. Banday, L. Bedini, C. Burigana, G. Zotti, K. G'orski, E. Salerno. 2001

159 citations recorded by the source corpus.

We present a new, fast, algorithm for the separation of astrophysical components superposed in maps of the sky. The algorithm, based on the Independent Component Analysis (ICA) technique, is aimed at recovering both the spatial pattern and the frequency scalings of the emissions from statistically independent astrophysical processes, present along the line-of-sight, from multi-frequency observations, without any a priori assumption on properties of the components to be separated, except that all of them, but at most one, must have non-Gaussian distributions. The analysis starts from very simple toy-models of the sky emission in order to assess the quality of the reconstruction when inputs are well known and controlled. In particular we study the dependence of the results of separation conducted on and off the Galactic plane independently, showing that optimal separation is achieved for sky regions where components are smoothly distributed. Then we move to more realistic applications on simulated observations of the microwave sky with angular resolution and instrumental noise at the mean nominal levels for the Planck satellite. We consider several Planck observation channels containing the most important known diffuse signals: the Cosmic Microwave Background (CMB), Galactic synchrotron, dust and free-free emissions. A method for calibrating the reconstructed maps of each component at each frequency has been devised. The spatial pattern of all the components have been recovered on all scales probed by the instrument. In particular, the CMB angular power spectra is recovered at the percent level up to lmax ≃ 2000. Frequency scalings and normalization have been recovered with better than 1% precision for all the components at frequencies and in sky regions where their signalto-noise ratio > ∼ 1.5; the error increases at ∼ 10% level for signal-to-noise ratios ≃ 1.

Subfields: Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a50b780c6c1af9e7a18e6c4f633eafc9927b98c8

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Citations in the corpus

Component separation methods for the PLANCK mission (2008, unknown): “– Independent component analysis(FastICA; Maino et al. 2002).”
Multidetector multicomponent spectral matching and applications for cosmic microwave background data analysis (2002, methodology): “The work of Baccigalupi et al. (2000), further extended by Maino et al. (2002) implements a blind source separation method exploiting the non–Gaussianity of the sources for their separation.”
A full sky, low foreground, high resolution CMB map from WMAP (2008, methodology): “Maino et al. (2007) obtain also several CMB maps, using the FastICA method (Maino et al. 2002).”
CMB and SZ effect separation with constrained Internal Linear Combinations (2010, methodology): “Blind component separation methods such as SMICA (Delabrouille, Cardoso & Patanchon 2003; Cardoso et al. 2008), FastICA (Hyvarinen 1999; Maino et al. 2002), JADE (Cardoso 1998), CCA (Bonaldi et al. 2006) or GMCA (Bobin et al. 2008) are primarily designed to solve the problem of estimating the…”
Analysis of the emission of very small dust particles from Spitzer spectro-imagery data using blind signal separation methods (2007, methodology): “…Astrophysics, ICA has been sucessfully used for spectra discrimination in infrared spectro-imagery of Mars ices (Forni et al. 2005), elimination of artifacts in astronomical images (Funaro et al. 2003) or extraction of cosmic microwave background signal inPlanck simulated data (Maino et al. 2002).”