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To correctly analyse data sets from current microwave detection technology, one is forced to estimate the sky signal and experimental noise simultaneously. Given a time-ordered data set we propose a formalism and method for estimating the signal and associated errors without prior knowledge of the noise power spectrum. We derive the method using a Bayesian formalism and relate it to the standard methods; in particular we show how this leads to a change in the estimate of the noise covariance matrix of the sky signal. We study the convergence and accuracy of the method on two mock observational strategies and discuss its application to a currently-favoured calibration procedure.
In this lecture, after a synthetic review of measurements of CMB temperature anisotropies and of their cosmological implications, the theoretical background of CMB polarization is summarized and the concepts of the main experiments that are ongoing or are being planned are briefly described.
The desire for higher sensitivity has driven ground-based cosmic microwave background (CMB) experiments to employ ever larger focal planes, which in turn require larger reimaging optics. Practical limits to the maximum size of these optics motivates
The large size of the time ordered data of cosmic microwave background experiments presents challenges for mission planning and data analysis. These issues are particularly significant for Antarctica- and space-based experiments, which depend on sate
The observation of cosmic microwave background (CMB) anisotropies is one of the key probes of physical cosmology. The weak nature of this signal has driven the construction of increasingly complex and sensitive experiments observing the sky at multip
An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background (CMB). A Bayesian approach is taken, based on modeling the string signal in the wavelet domain with generalized Gaussian distributions. Goo