Efficient least-squares basket-weaving

TL;DR

This paper introduces an efficient least-squares method for basket-weaving in radio astronomy, improving computational speed and robustness while enabling masking of bad data, validated through simulations and real telescope data.

Contribution

A novel linear least-squares approach for basket-weaving that handles arbitrary sampling, enhances efficiency, robustness, and allows masking of contaminated data.

Findings

Improved computational efficiency over previous methods

Enhanced robustness to data irregularities

Effective masking of radio frequency interference

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.