We model a 21 cm intensity mapping survey in the redshift range 0.01<z<1.5 designed to simulate the skies as seen by future radio telescopes such as the Square Kilometre Array (SKA), including instrumental noise and Galactic foregrounds. In our pipel
ine, we remove the introduced Galactic foregrounds with a fast independent component analysis (fastica) technique. We present the power spectrum of the large-scale matter distribution, C(l), before and after the application of this foreground removal method and calculate the resulting systematic errors. We attempt to reduce systematics in the foreground subtraction by optimally masking the maps to remove high foregrounds in the Galactic plane. Our simulations show a certain level of bias remains in the power spectrum at all scales l<400. At large-scales l<30 this bias is particularly significant. We measure the impact of these systematic effects in two different ways: firstly we fit cosmological parameters to the broadband shape of the power spectrum and secondly we extract the position of the Baryon Acoustic Oscillations (BAO). In the first analysis, we find that the systematics introduce an significant shift in the best fit cosmological parameters at the 2 to 3 sigma level which depends on the masking and noise levels. However, cosmic distances can be recovered in an unbiased way after foreground removal at all simulated redshifts by fitting the BAOs in the power spectrum. We conclude that further advances in foreground removal are needed in order to recover unbiased information from the broadband shape of the power spectrum, however, intensity mapping experiments will be a powerful tool for mapping cosmic distances across a wide redshift range.
Next-generation radio interferometric telescopes will exhibit non-coplanar baseline configurations and wide field-of-views, inducing a w-modulation of the sky image, which in turn induces the spread spectrum effect. We revisit the impact of this effe
ct on imaging quality and study a new algorithmic strategy to deal with the associated operator in the image reconstruction process. In previous studies it has been shown that image recovery in the framework of compressed sensing is improved due to the spread spectrum effect, where the w-modulation can act to increase the incoherence between measurement and sparsifying signal representations. For the purpose of computational efficiency, idealised experiments were performed, where only a constant baseline component w in the pointing direction of the telescope was considered. We extend this analysis to the more realistic setting where the w-component varies for each visibility measurement. Firstly, incorporating varying w-components into imaging algorithms is a computational demanding task. We propose a variant of the w-projection algorithm for this purpose, which is based on an adaptive sparsification procedure, and incorporate it in compressed sensing imaging methods. This sparse matrix variant of the w-projection algorithm is generic and adapts to the support of each kernel. Consequently, it is applicable for all types of direction-dependent effects. Secondly, we show that for w-modulation with varying w-components, reconstruction quality is significantly improved compared to the setting where there is no w-modulation (i.e. w=0), reaching levels comparable to the quality of a constant, maximal w-component. This finding confirms that one may seek to optimise future telescope configurations to promote large w-components, thus enhancing the spread spectrum effect and consequently the fidelity of image reconstruction.
