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Register a new userBlind and non-blind source detection in WMAP 5-year maps
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We have analyzed the efficiency in source detection and flux density estimation of blind and non-blind detection techniques exploiting the MHW2 filter applied to the Wilkinson Microwave Anisotropy Probe (WMAP) 5-year maps. A comparison with the AT20G Bright Source Sample (Massardi et al. 2008), with a completeness limit of 0.5 Jy and accurate flux measurements at 20 GHz, close to the lowest frequency of WMAP maps, has allowed us to assess the completeness and the reliability of the samples detected with the two approaches, as well as the accuracy of flux and error estimates, and their variations across the sky. The uncertainties on flux estimates given by our procedure turned out to be about a factor of 2 lower than the rms differences with AT20G measurements, consistent with the smoothing of the fluctuation field yielded by map filtering. Flux estimates were found to be essentially unbiased except that, close to the detection limit, a substantial fraction of fluxes are found to be inflated by the contribution of underlying positive fluctuations. This is consistent with expectations for the Eddington bias associated to the true errors on flux density estimates. The blind and non-blind approaches are found to be complementary: each of them allows the detection of sources missed by the other. Combining results of the two methods on the WMAP 5-year maps we have expanded the non-blindly generated New Extragalactic WMAP Point Source (NEWPS) catalogue (Lopez-Caniego et al. 2007) that was based on WMAP 3-year maps. After having removed the probably spurious objects not identified with known radio sources, the new version of the NEWPS catalogue, NEWPS_5yr comprises 484 sources detected with a signal-to-noise ratio SNR>5.
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Recently a blind source separation model was suggested for spatial data together with an estimator based on the simultaneous diagonalisation of two scatter matrices. The asymptotic properties of this estimator are derived here and a new estimator, based on the joint diagonalisation of more than two scatter matrices, is proposed. The asymptotic properties and merits of the novel estimator are verified in simulation studies. A real data example illustrates the method.
Regional data analysis is concerned with the analysis and modeling of measurements that are spatially separated by specifically accounting for typical features of such data. Namely, measurements in close proximity tend to be more similar than the ones further separated. This might hold also true for cross-dependencies when multivariate spatial data is considered. Often, scientists are interested in linear transformations of such data which are easy to interpret and might be used as dimension reduction. Recently, for that purpose spatial blind source separation (SBSS) was introduced which assumes that the observed data are formed by a linear mixture of uncorrelated, weakly stationary random fields. However, in practical applications, it is well-known that when the spatial domain increases in size the weak stationarity assumptions can be violated in the sense that the second order dependency is varying over the domain which leads to non-stationary analysis. In our work we extend the SBSS model to adjust for these stationarity violations, present three novel estimators and establish the identifiability and affine equivariance property of the unmixing matrix functionals defining these estimators. In an extensive simulation study, we investigate the performance of our estimators and also show their use in the analysis of a geochemical dataset which is derived from the GEMAS geochemical mapping project.
Multi-wavelength solar images in the EUV are routinely used for analysing solar features such as coronal holes, filaments, and flares. However, images taken in different bands often look remarkably similar as each band receives contributions coming from regions with a range of different temperatures. This has motivated the search for empirical techniques that may unmix these contributions and concentrate salient morphological features of the corona in a smaller set of less redundant source images. Blind Source Separation (BSS) precisely does this. Here we show how this novel concept also provides new insight into the physics of the solar corona, using observations made by SDO/AIA. The source images are extracted using a Bayesian positive source separation technique. We show how observations made in six spectral bands, corresponding to optically thin emissions, can be reconstructed by linear combination of three sources. These sources have a narrower temperature response and allow for considerable data reduction since the pertinent information from all six bands can be condensed in only one single composite picture. In addition, they give access to empirical temperature maps of the corona. The limitations of the BSS technique and some applications are briefly discussed.
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 Gaussianity analysis of the WMAP 5-year Cosmic Microwave Background (CMB) temperature anisotropy data maps. We use several third order estimators based on the spherical Mexican hat wavelet. We impose constraints on the local non-linear coupling parameter fnl using well motivated non-Gaussian simulations. We analyse the WMAP maps at resolution of 6.9 arcmin for the Q, V, and W frequency bands. We use the KQ75 mask recommended by the WMAP team which masks out 28% of the sky. The wavelet coefficients are evaluated at 10 different scales from 6.9 to 150 arcmin. With these coefficients we compute the third order estimators which are used to perform a chi-squared analysis. The chi-squared statistic is used to test the Gaussianity of the WMAP data as well as to constrain the fnl parameter. Our results indicate that the WMAP data are compatible with the Gaussian simulations, and the fnl parameter is constrained to -8 < fnl < +111 at 95% CL for the combined V+W map. This value has been corrected for the presence of undetected point sources, which add a positive contribution of Delta_fnl = 3+-5 in the V+W map. Our results are very similar to those obtained by Komatsu et al (2008) using the bispectrum.
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