Efficient least-squares basket-weaving

Abstract

We report on a novel method to solve the basket-weaving problem. Basket-weaving is a technique that is used to remove scan-line patterns from single-dish radio maps. The new approach applies linear least-squares and works on gridded maps from arbitrarily sampled data, which greatly improves computational efficiency and robustness. It also allows masking of bad data, which is useful for cases where radio frequency interference is present in the data. We evaluate the algorithms using simulations and real data obtained with the Effelsberg 100-m telescope.

11 pages, 11 figures, accepted by A&A

References in corpus (3)

• High-resolution wide-band Fast Fourier Transform spectrometers
• Unbiased flux calibration methods for spectral-line radio observations
• Effects of Destriping Errors on CMB Polarisation Power Spectra and and Pixel Noise Covariances

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