No Arabic abstract
A flexible maximum-entropy component separation algorithm is presented that accommodates anisotropic noise, incomplete sky-coverage and uncertainties in the spectral parameters of foregrounds. The capabilities of the method are determined by first applying it to simulated spherical microwave data sets emulating the COBE-DMR, COBE-DIRBE and Haslam surveys. Using these simulations we find that is very difficult to determine unambiguously the spectral parameters of the galactic components for this data set due to their high level of noise. Nevertheless, we show that is possible to find a robust CMB reconstruction, especially at the high galactic latitude. The method is then applied to these real data sets to obtain reconstructions of the CMB component and galactic foreground emission over the whole sky. The best reconstructions are found for values of the spectral parameters: T_d=19 K, alpha_d=2, beta_ff=-0.19 and beta_syn=-0.8. The CMB map has been recovered with an estimated statistical error of sim 22 muK on an angular scale of 7 degrees outside the galactic cut whereas the low galactic latitude region presents contamination from the foreground emissions.
The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an intrinsic and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{parallel} le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $sim 1$ MHz (dominating over $k_{parallel} sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $sim 0.8$ MHz.
This paper offers a new point of view on component separation, based on a model of additive components which enjoys a much greater flexibility than more traditional linear component models. This flexibility is needed to process the complex full-sky observations of the CMB expected from the Planck space mission, for which it was developed, but it may also be useful in any context where accurate component separation is needed.
We present an application of the fast Independent Component Analysis method to the COBE-DMR 4yr data. Although the signal-to-noise ratio in the COBE-DMR data is typically $sim 1$, the approach is able to extract the CMB signal with high confidence when working at high galactic latitudes. The reconstructed CMB map shows the expected frequency scaling of the CMB. We fit the resulting CMB component for the rms quadrupole normalisation Qrms and primordial spectral index n and find results in excellent agreement with those derived from the minimum-noise combination of the 90 and 53 GHz DMR channels without galactic emission correction. Including additional channels (priors) such as the Haslam map of radio emission at 408 MHz and the DIRBE 140um map of galactic infra-red emission, the FastICA algorithm is able to both detect galactic foreground emission and separate it from the dominant CMB signal. Fitting the resulting CMB component for Qrms and n we find good agreement with the results from Gorski et al.(1996) in which the galactic emission has been taken into account by subtracting that part of the DMR signal observed to be correlated with these galactic template maps. We further investigate the ability of FastICA to evaluate the extent of foreground contamination in the COBE-DMR data. We include an all-sky Halpha survey (Dickinson, Davies & Davis 2003) to determine a reliable free-free template. In particular we find that, after subtraction of the thermal dust emission predicted by the Finkbeiner, Davis & Schlegel (1999) model 7, this component is the dominant foreground emission at 31.5 GHz. This indicates the presence of an anomalous dust correlated component which is well fitted by a power law spectral shape $ u^{-beta}$ with $beta sim 2.5$ in agreement with Banday et al. (2003).
In Monte Carlo simulation, lattice field theory with a $theta$ term suffers from the sign problem. This problem can be circumvented by Fourier-transforming the topological charge distribution $P(Q)$. Although this strategy works well for small lattice volume, effect of errors of $P(Q)$ becomes serious with increasing volume and prevents one from studying the phase structure. This is called flattening. As an alternative approach, we apply the maximum entropy method (MEM) to the Gaussian $P(Q)$. It is found that the flattening could be much improved by use of the MEM.
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.