摘要：The redshifted 21 cm signal from neutral hydrogen (H I ) is potentially a very powerful probe for cosmology, but a difficulty in its observation is that it is much weaker than foreground radiation from the Milky Way as well as extragalactic radio sources. The foreground radiation at different frequencies, however, is coherent along one line of sight, and various methods of foreground subtraction based on this property have been proposed. In this paper, we present a new method based on robust principal component analysis (RPCA) to subtract the foreground and extract the 21 cm signal, which explicitly uses both the low-rank property of the frequency covariance matrix (i.e., frequency coherence) of the foreground and the sparsity of the frequency covariance matrix of the 21 cm signal. The low-rank property of the foreground’s frequency covariance has been exploited in many previous works on foreground subtraction, but to our knowledge the sparsity of the frequency covariance of the 21cm signal is first explored here. By exploiting both properties in the RPCA method, the frequency covariance matrix of the foreground and the 21 cm signal can be separated accurately. We then use the generalized internal linear combination method to recover the 21 cm signal from the data. Our method is applicable both to a small patch of sky with the flat-sky approximation and to a large area of sky where the sphericity has to be considered. It is also easy to extend to deal with more complex conditions such as a sky map with defects.
关键词：cosmology: observations;methods: data analysis;methods: statistical;techniques: image processing
