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Register a new userSparse point-source removal for full-sky CMB experiments: application to WMAP 9-year data
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Missions such as WMAP or Planck measure full-sky fluctuations of the cosmic microwave background and foregrounds, among which bright compact source emissions cover a significant fraction of the sky. To accurately estimate the diffuse components, the point-source emissions need to be separated from the data, which requires a dedicated processing. We propose a new technique to estimate the flux of the brightest point sources using a morphological separation approach: point sources with known support and shape are separated from diffuse emissions that are assumed to be sparse in the spherical harmonic domain. This approach is compared on both WMAP simulations and data with the standard local chi2 minimization, modelling the background as a low-order polynomial. The proposed approach generally leads to 1) lower biases in flux recovery, 2) an improved root mean-square error of up to 35% and 3) more robustness to background fluctuations at the scale of the source. The WMAP 9-year point-source-subtracted maps are available online.
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A well-tested and validated Gibbs sampling code, that performs component separation and CMB power spectrum estimation, was applied to the {it WMAP} 5-yr data. Using a simple model consisting of CMB, noise, monopoles and dipoles, a ``per pixel low-frequency power-law (fitting for both amplitude and spectral index), and a thermal dust template with fixed spectral index, we found that the low-$ell$ ($ell < 50$) CMB power spectrum is in good agreement with the published {it WMAP}5 results. Residual monopoles and dipoles were found to be small ($lesssim 3 mu$K) or negligible in the 5-yr data. We comprehensively tested the assumptions that were made about the foregrounds (e.g. dust spectral index, power-law spectral index prior, templates), and found that the CMB power spectrum was insensitive to these choices. We confirm the asymmetry of power between the north and south ecliptic hemispheres, which appears to be robust against foreground modeling. The map of low frequency spectral indices indicates a steeper spectrum on average ($beta=-2.97pm0.21$) relative to those found at low ($sim$GHz) frequencies.
We perform a blind multi-component analysis of the WMAP 1 year foreground cleaned maps using SMICA (Spectral Matching Independent Component Analysis). We provide a new estimate of the CMB power spectrum as well as the amplitude of the CMB anisotropies across frequency channels. We show that the CMB anisotropies are compatible with temperature fluctuations as expected from the standard paradigm. The analysis also allows us to identify and separate a weak residual galactic emission present significantly in the Q-band outside of the Kp2 mask limits, and mainly concentrated at low galactic latitudes. We produce a map of this residual component by Wiener filtering using estimated parameters. The level of contamination of CMB data by this component is compatible with the WMAP team estimation of foreground residual contamination. In addition, the multi-component analysis allows us to estimate jointly the power spectrum of unresolved point source emission.
The detection of point sources in Cosmic Microwave Background maps is usually based on a single-frequency approach, whereby maps at each frequency are filtered separately and the spectral information on the sources is derived combining the results at the different frequencies. On the contrary, in the case of multi-frequency detection methods, source detection and spectral information are tightly interconnected in order to increase the source detection efficiency. In this work we apply the emph{matched multifilter} method to the detection of point sources in the WMAP 7yr data at 61 and 94 GHz. This linear filtering technique takes into account the spatial and the cross-power spectrum information at the same time using the spectral behaviour of the sources without making any a priori assumption about it. We follow a two-step approach. First, we do a blind detection of the sources over the whole sky. Second, we do a refined local analysis at their positions to improve the signal-to-noise ratio of the detections. At 94 GHz we detect 129 5{sigma} objects at |b|>5{deg} (excluding the Large Magellanic Cloud region); 119 of them are reliable extragalactic sources and 104 of these 119 lie outside the WMAP Point Source Catalog mask. Nine of the total 129 detections are known Galactic sources or lie in regions of intense Galactic emission and one additional (weak) high-Galactic latitude source has no counterpart in low-frequency radio catalogues. Our results constitute a substantial improvement over the NEWPS-3year catalogue.
We present a new method based on phase analysis for the Galaxy and foreground component separation from the cosmic microwave background (CMB) signal. This method is based on a prevailing assumption that the phases of the underlying CMB signal should have no or little correlation with those of the foregrounds. This method takes into consideration all the phases of each multipole mode (l <= 50, -l <= m <=l) from the whole sky without galactic cut, masks or any dissection of the whole sky into disjoint regions. We use cross correlation of the phases to illustrate that significant correlations of the foregrounds manifest themselves in the phases of the WMAP 5 frequency bands, which are used for separation of the CMB from the signals. Our final phase-cleaned CMB map has the angular power spectrum in agreement with both the WMAP result and that from Tegmark, de Oliveira-Costa and Hamilton (TOH), the phases of our derived CMB signal, however, are slightly different from those of the WMAP Internal Linear Combination map and the TOH map.
We present ARC2 (Astrophysically Robust Correction 2), an open-source Python-based systematics-correction pipeline to correct for the Kepler prime mission long cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves, then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with `shrinkage priors to minimise the risk of over-fitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipelines performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.
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