Abstract:
The Sun is a difficult radio source to study, but radio observations of the Sun carry crucial
information about solar magnetic fields and coronal processes inaccessible by any other
bands in the EM spectrum. Recently, instruments with capabilities matching the needs of
solar radio physics have begun to become available. The Murchison Widefield Array
(MWA), commissioned in mid-2013, is one such instrument which already has ∼3000 hours
of solar observations. Conventional radio interferometeric imaging is very effort and
computation intensive. As a part of building the ability to analyse large volumes of MWA
data in an unsupervised manner, an automated imaging pipeline AIRCARS [8] has recently
been developed. It routinely produces images with dynamic range between ∼1000 to
∼100,000, exceeding that of the earlier state of the art by two to three orders of
magnitude. Along with the opportunity to strengthen our understanding of the Sun, every
significant technological advance in solar observations has been accompanied by surprising
new discoveries. This project aims to explore the potential of this newly acquired imaging capability. Given the very large computational burden of interferometric imaging, a key part of the problem is choosing the data to be analysed. This project explores a few different strategies for making this choice in an unbiased manner. We present the results from analysing some of the data so chosen using the imaging pipeline.
