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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.
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 acc
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 obs
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 analys
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 impl
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 appro