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Register a new userNeedlet thresholding methods in component separation
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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.
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We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to Generalized Morphological Component Analysis (GMCA), we introduce Hierarchical GMCA (HGMCA), a Bayesian hierarchical graphical model for source separation. We test our method on $N_{rm side}=256$ simulated sky maps that include dust, synchrotron, free-free and anomalous microwave emission, and show that HGMCA reduces foreground contamination by $25%$ over GMCA in both the regions included and excluded by the Planck UT78 mask, decreases the error in the measurement of the CMB temperature power spectrum to the $0.02-0.03%$ level at $ell>200$ (and $<0.26%$ for all $ell$), and reduces correlation to all the foregrounds. We find equivalent or improved performance when compared to state-of-the-art Internal Linear Combination (ILC)-type algorithms on these simulations, suggesting that HGMCA may be a competitive alternative to foreground separation techniques previously applied to observed CMB data. Additionally, we show that our performance does not suffer when we perturb model parameters or alter the CMB realization, which suggests that our algorithm generalizes well beyond our simplified simulations. Our results open a new avenue for constructing CMB maps through Bayesian hierarchical analysis.
Observing the neutral Hydrogen (HI) distribution across the Universe via redshifted 21-cm line Intensity Mapping (IM) constitutes a powerful probe for cosmology. However, this redshifted 21cm signal is obscured by the foreground emission. This paper addresses the capabilities of the BINGO survey to separate such signals. Specifically, this paper (a) looks in detail at the different residuals left over by foreground components, (b) shows that a noise-corrected spectrum is unbiased and (c) shows that we understand the remaining systematic residuals by analyzing non-zero contributions to the three point function. We use the Generalized Needlet Internal Linear Combination (GNILC), which we apply to sky simulations of the BINGO experiment for each redshift bin of the survey. We present our recovery of the redshifted 21-cm signal from sky simulations of the BINGO experiment including foreground components. We test the recovery of the 21-cm signal through the angular power spectrum at different redshifts, as well as the recovery of its non-Gaussian distribution through a bispectrum analysis. We find that non-Gaussianities from the original foreground maps can be removed down to, at least, the noise limit of the BINGO survey with such techniques. Our bispectrum analysis yields strong tests of the level of the residual foreground contamination in the recovered 21-cm signal, thereby allowing us to both optimize and validate our component separation analysis. (Abridged)
The large-scale distribution of neutral hydrogen (HI) in the Universe is luminous through its 21-cm emission. The goal of the Baryon Acoustic Oscillations from Integrated Neutral Gas Observations - BINGO radio telescope is to detect Baryon Acoustic Oscillations (BAO) at radio frequencies through 21-cm Intensity Mapping (IM). The telescope will span the redshift range 0.127 $< z <$ 0.449 with an instantaneous field-of-view of $14.75^{circ} times 6.0^{circ}$. In this work, we investigate different constructive and operational scenarios of the instrument by generating sky maps as they should be produced by the instrument. In doing this we use a set of end-to-end IM mission simulations. The maps will additionally also be used to evaluate the efficiency of a component separation method (GNILC). We have simulated the kind of data that would be produced in a single-dish IM experiment like BINGO. According to the results obtained we have optimized the focal plane design of the telescope. In addition, the application of the GNILC method on simulated data shows that it is feasible to extract the cosmological signal across a wide range of multipoles and redshifts. The results are comparable with the standard PCA method.
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. 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 of different physical origin. A component separation challenge has been organized, 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. Different methods proved to be effective in cleaning the CMB maps from 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 that of 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. 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.
The Internal Linear Combination (ILC) component separation method has been extensively used to extract a single component, the CMB, from the WMAP multifrequency data. We generalise the ILC approach for separating other millimetre astrophysical emissions. We construct in particular a multidimensional ILC filter, which can be used, for instance, to estimate the diffuse emission of a complex component originating from multiple correlated emissions, such as the total emission of the Galactic interstellar medium. The performance of such generalised ILC methods, implemented on a needlet frame, is tested on simulations of Planck mission observations, for which we successfully reconstruct a low noise estimate of emission from astrophysical foregrounds with vanishing CMB and SZ contamination.
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