Sparsity and Morphological Diversity in Blind Source Separation

Bobin, J.; Starck, Jean-Luc; Fadili, Jalal M.; Moudden, Y.

doi:10.1109/tip.2007.906256

Cited by 189 publications

(161 citation statements)

References 30 publications

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“…We use GMCA (Bobin et al 2007a(Bobin et al , 2007b(Bobin et al , 2007c(Bobin et al , 2008, specifically tailored to foreground removal (Chapman et al 2013), to remove the dominant modes from the data cubes in Stokes I and any remaining instrumental polarization leakage in Stokes V.…”

Section: Generalized Morphological Component Analysismentioning

confidence: 99%

Upper Limits on the 21 cm Epoch of Reionization Power Spectrum from One Night with LOFAR

Patil

Yatawatta

Koopmans

et al. 2017

ApJ

291

374

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We present the first limits on the Epoch of Reionization 21 cm H I power spectra, in the redshift range z=7.9-10.6, using the Low-Frequency Array (LOFAR) High-Band Antenna (HBA). In total, 13.0 hr of data were used from observations centered on the North Celestial Pole. After subtraction of the sky model and the noise bias, we detect a non-zero 56 13 mK D < ( ) at k=0.053 h cMpc −1 in the range z=9.6-10.6. The excess variance decreases when optimizing the smoothness of the direction-and frequency-dependent gain calibration, and with increasing the completeness of the sky model. It is likely caused by (i) residual side-lobe noise on calibration baselines, (ii) leverage due to nonlinear effects, (iii) noise and ionosphere-induced gain errors, or a combination thereof. Further analyses of the excess variance will be discussed in forthcoming publications.

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Section: Generalized Morphological Component Analysismentioning

confidence: 99%

Upper Limits on the 21 cm Epoch of Reionization Power Spectrum from One Night with LOFAR

Patil

Yatawatta

Koopmans

et al. 2017

ApJ

291

374

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“…In Bobin et al (2007), it was shown that sparsity enhances the diversity between the components thus improving the separation quality. The spectral signatures of CMB and SZ are assumed to be known.…”

Section: A3 Generalised Morphological Component Analysis (Gmca)mentioning

confidence: 99%

Component separation methods for the PLANCK mission

Leach

Cardoso

Baccigalupi

et al. 2008

A&A

218

231

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Context. The planck satellite will map the full sky at nine frequencies from 30 to 857 GHz. The CMB intensity and polarization that are its prime targets are contaminated by foreground emission. Aims. The goal of this paper is to compare proposed methods for separating CMB from foregrounds based on their different spectral and spatial characteristics, and to separate the foregrounds into "components" with different physical origins (Galactic synchrotron, free-free and dust emissions; extra-galactic and far-IR point sources; Sunyaev-Zeldovich effect, etc.). Methods. A component separation challenge has been organised, based on a set of realistically complex simulations of sky emission. Several methods including those based on internal template subtraction, maximum entropy method, parametric method, spatial and harmonic cross correlation methods, and independent component analysis have been tested. Results. Different methods proved to be effective in cleaning the CMB maps of foreground contamination, in reconstructing maps of diffuse Galactic emissions, and in detecting point sources and thermal Sunyaev-Zeldovich signals. The power spectrum of the residuals is, on the largest scales, four orders of magnitude lower than the input Galaxy power spectrum at the foreground minimum. The CMB power spectrum was accurately recovered up to the sixth acoustic peak. The point source detection limit reaches 100 mJy, and about 2300 clusters are detected via the thermal SZ effect on two thirds of the sky. We have found that no single method performs best for all scientific objectives.Conclusions. We foresee that the final component separation pipeline for planck will involve a combination of methods and iterations between processing steps targeted at different objectives such as diffuse component separation, spectral estimation, and compact source extraction.

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“…it is actually part of n. We can expand the data x in a PoS(AASKA14)005 CD/EoR Foreground Removal Emma Chapman wavelet basis and seek an unmixing scheme, through the estimation of A, which yields the sparsest components s in the wavelet domain. For more technical details about GMCA, we refer the interested reader to Bobin et al (2007Bobin et al ( , 2008aBobin et al ( ,b, 2013, where it is shown that sparsity, as used in GMCA, allows for a more precise estimation of the mixing matrix A and more robustness to noise than ICA-based techniques such as FastICA. For a previous application of GMCA to EoR data see Chapman et al (2013).…”

Section: Gmcamentioning

confidence: 99%

Cosmic Dawn and Epoch of Reionization Foreground Removal with the SKA

Chapman¹,

Bonaldi²,

Harker³

et al. 2015

Proceedings of Advancing Astrophysics With the Square Kilometre Array — PoS(AASKA14)

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The exceptional sensitivity of the SKA will allow observations of the Cosmic Dawn and Epoch of Reionization (CD/EoR) in unprecedented detail, both spectrally and spatially. This wealth of information is buried under Galactic and extragalactic foregrounds, which must be removed accurately and precisely in order to reveal the cosmological signal. This problem has been addressed already for the previous generation of radio telescopes, but the application to SKA is different in many aspects. We summarise the contributions to the field of foreground removal in the context of high redshift and high sensitivity 21-cm measurements. We use a state-of-the-art simulation of the SKA Phase 1 observations complete with cosmological signal, foregrounds and frequencydependent instrumental effects to test both parametric and non-parametric foreground removal methods. We compare the recovered cosmological signal using several different statistics and explore one of the most exciting possibilities with the SKA -imaging of the ionized bubbles. We find that with current methods it is possible to remove the foregrounds with great accuracy and to get impressive power spectra and images of the cosmological signal. The frequency-dependent PSF of the instrument complicates this recovery, so we resort to splitting the observation bandwidth into smaller segments, each of a common resolution. If the foregrounds are allowed a random variation from the smooth power law along the line of sight, methods exploiting the smoothness of foregrounds or a parametrization of their behaviour are challenged much more than non-parametric ones. However, we show that correction techniques can be implemented to restore the performances of parametric approaches, as long as the first-order approximation of a power law stands.Advancing Astrophysics with the Square Kilometre Array

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Sparsity and Morphological Diversity in Blind Source Separation

Cited by 189 publications

References 30 publications

Upper Limits on the 21 cm Epoch of Reionization Power Spectrum from One Night with LOFAR

Upper Limits on the 21 cm Epoch of Reionization Power Spectrum from One Night with LOFAR

Component separation methods for the PLANCK mission

Cosmic Dawn and Epoch of Reionization Foreground Removal with the SKA

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