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Fast Independent Component Analysis (FastICA) is a component separation algorithm based on the levels of non-Gaussianity. Here we apply the FastICA to the component separation problem of the microwave background including carbon monoxide (CO) line emissions that are found to contaminate the PLANCK High Frequency Instrument (HFI) data. Specifically we prepare 100GHz, 143GHz, and 217GHz mock microwave sky maps including galactic thermal dust, NANTEN CO line, and the Cosmic Microwave Background (CMB) emissions, and then estimate the independent components based on the kurtosis. We find that the FastICA can successfully estimate the CO component as the first independent component in our deflection algorithm as its distribution has the largest degree of non-Gaussianity among the components. By subtracting the CO and the dust components from the original sky maps, we will be able to make an unbiased estimate of the cosmological CMB angular power spectrum.
We present a data analysis pipeline for CMB polarization experiments, running from multi-frequency maps to the power spectra. We focus mainly on component separation and, for the first time, we work out the covariance matrix accounting for errors ass
We present a novel technique for Cosmic Microwave Background (CMB) foreground subtraction based on the framework of blind source separation. Inspired by previous work incorporating local variation to Generalized Morphological Component Analysis (GMCA
Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which co
Independent component analysis (ICA) has been widely used for blind source separation in many fields such as brain imaging analysis, signal processing and telecommunication. Many statistical techniques based on M-estimates have been proposed for esti
Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the a