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We present the first application of a new foreground removal pipeline to thecurrent leading HI intensity mapping dataset, obtained by the Green BankTelescope (GBT). We study the 15hr and 1hr field data of the GBT observationspreviously presented in Masui et al (2013) and Switzer et al (2013), coveringabout 41 square degrees at 0.6<z<1.0, for which cross-correlations may bemeasured with the galaxy distribution of the WiggleZ Dark Energy Survey. In thepresented pipeline, we subtract the Galactic foreground continuum and the pointsource contamination using an independent component analysis technique(fastica), and develop a Fourier-based optimal estimator to compute thetemperature power spectrum of the intensity maps and cross-correlation with thegalaxy survey data. We show that fastica is a reliable tool to subtract diffuseand point-source emission through the non-Gaussian nature of their probabilitydistributions. The temperature power spectra of the intensity maps is dominatedby instrumental noise on small scales which fastica, as a conservativesubtraction technique of non-Gaussian signals, can not mitigate. However, wedetermine similar GBT-WiggleZ cross-correlation measurements to those obtainedby the Singular Value Decomposition (SVD) method, and confirm that foregroundsubtraction with fastica is robust against 21cm signal loss, as seen by theconverged amplitude of these cross-correlation measurements. We conclude thatSVD and fastica are complementary methods to investigate the foregrounds andnoise systematics present in intensity mapping datasets.

Erasing the Milky Way: new cleaning technique applied to GBT intensity mapping data