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Register a new userForeground separation methods for satellite observations of the cosmic microwave background
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A maximum entropy method (MEM) is presented for separating the emission due to different foreground components from simulated satellite observations of the cosmic microwave background radiation (CMBR). In particular, the method is applied to simulated observations by the proposed Planck Surveyor satellite. The simulations, performed by Bouchet and Gispert (1998), include emission from the CMBR, the kinetic and thermal Sunyaev-Zeldovich (SZ) effects from galaxy clusters, as well as Galactic dust, free-free and synchrotron emission. We find that the MEM technique performs well and produces faithful reconstructions of the main input components. The method is also compared with traditional Wiener filtering and is shown to produce consistently better results, particularly in the recovery of the thermal SZ effect.
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Simulated observations of a $10dg times 10dg$ field by the Microwave Anisotropy Probe (MAP) are analysed in order to separate cosmic microwave background (CMB) emission from foreground contaminants and instrumental noise and thereby determine how accurately the CMB emission can be recovered. The simulations include emission from the CMB, the kinetic and thermal Sunyaev-Zeldovich (SZ) effects from galaxy clusters, as well as Galactic dust, free-free and synchrotron. We find that, even in the presence of these contaminating foregrounds, the CMB map is reconstructed with an rms accuracy of about 20 $mu$K per 12.6 arcmin pixel, which represents a substantial improvement as compared to the individual temperature sensitivities of the raw data channels. We also find, for the single $10dg times 10dg$ field, that the CMB power spectrum is accurately recovered for $ell la 600$.
We study the effect of extragalactic point sources on satellite observations of the cosmic microwave background (CMB). In order to separate the contributions due to different foreground components, a maximum-entropy method is applied to simulated observations by the Planck Surveyor satellite. In addition to point sources, the simulations include emission from the CMB and the kinetic and thermal Sunyaev-Zeldovich (SZ) effects from galaxy clusters, as well as Galactic dust, free-free and synchrotron emission. We find that the main input components are faithfully recovered and, in particular, that the quality of the CMB reconstruction is only slightly reduced by the presence of point sources. In addition, we find that it is possible to recover accurate point source catalogues at each of the Planck Surveyor observing frequencies.
We quantify the level of polarization of the atmosphere due to Zeeman splitting of oxygen in the Earths magnetic field and compare it to the level of polarization expected from the polarization of the cosmic microwave background radiation. The analysis focuses on the effect at mid-latitudes and at large angular scales. We find that from stratospheric balloon borne platforms and for observations near 100 GHz the atmospheric linear and circular polarized intensities is about 10^{-12} and 100 x 10^{-9} K, respectively, making the atmosphere a negligible source of foreground. From the ground the linear and circular polarized intensities are about 10^{-9} and 100 x 10^{-6} K, making the atmosphere a potential source of foreground for the CMB E (B) mode signal if there is even a 1% (0.01%) conversion of circular to linear polarization in the instrument.
We present results obtained with the PRONAOS balloon-borne experiment on interstellar dust. In particular, the submillimeter / millimeter spectral index is found to vary between roughly 1 and 2.5 on small scales (3.5 resolution). This could have implications for component separation in Cosmic Microwave Background maps.
In order to extract cosmological information from observations of the millimeter and submillimeter sky, foreground components must first be removed to produce an estimate of the cosmic microwave background (CMB). We developed a machine-learning approach for doing so for full-sky temperature maps of the millimeter and submillimeter sky. We constructed a Bayesian spherical convolutional neural network architecture to produce a model that captures both spectral and morphological aspects of the foregrounds. Additionally, the model outputs a per-pixel error estimate that incorporates both statistical and model uncertainties. The model was then trained using simulations that incorporated knowledge of these foreground components that was available at the time of the launch of the Planck satellite. On simulated maps, the CMB is recovered with a mean absolute difference of $<4mu$K over the full sky after masking map pixels with a predicted standard error of $>50mu$K; the angular power spectrum is also accurately recovered. Once validated with the simulations, this model was applied to Planck temperature observations from its 70GHz through 857GHz channels to produce a foreground-cleaned CMB map at a Healpix map resolution of NSIDE=512. Furthermore, we demonstrate the utility of the technique for evaluating how well different simulations match observations, particularly in regard to the modeling of thermal dust.
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