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

TL;DR

This paper introduces a new foreground removal pipeline using independent component analysis for GBT HI intensity mapping data, demonstrating its effectiveness and robustness in cross-correlation measurements with galaxy surveys.

Contribution

The paper presents a novel foreground subtraction pipeline employing fastica and develops a Fourier-based estimator for power spectrum analysis in HI intensity mapping.

Findings

fastica reliably subtracts non-Gaussian foregrounds

Cross-correlation results are consistent with previous methods

Foreground removal with fastica is robust against signal loss

Abstract

We present the first application of a new foreground removal pipeline to the current leading HI intensity mapping dataset, obtained by the Green Bank Telescope (GBT). We study the 15hr and 1hr field data of the GBT observations previously presented in Masui et al (2013) and Switzer et al (2013), covering about 41 square degrees at 0.6<z<1.0, for which cross-correlations may be measured with the galaxy distribution of the WiggleZ Dark Energy Survey. In the presented pipeline, we subtract the Galactic foreground continuum and the point source contamination using an independent component analysis technique (fastica), and develop a Fourier-based optimal estimator to compute the temperature power spectrum of the intensity maps and cross-correlation with the galaxy survey data. We show that fastica is a reliable tool to subtract diffuse and point-source emission through the non-Gaussian nature of their probability distributions. The temperature power spectra of the intensity maps is dominated by instrumental noise on small scales which fastica, as a conservative subtraction technique of non-Gaussian signals, can not mitigate. However, we determine similar GBT-WiggleZ cross-correlation measurements to those obtained by the Singular Value Decomposition (SVD) method, and confirm that foreground subtraction with fastica is robust against 21cm signal loss, as seen by the converged amplitude of these cross-correlation measurements. We conclude that SVD and fastica are complementary methods to investigate the foregrounds and noise systematics present in intensity mapping datasets.