The Murchison Widefield Array (MWA) routinely makes observations that are adversely affected by the Earth’s ionosphere. This means that the images and catalogues that are produced can be distorted and difficult to compare with data from other telescopes, and even with other MWA observations in different ionospheric conditions. We have developed algorithms to correct source positions for ionospheric effects, however they remain suboptimal, and some human intervention is occasionally required to filter out bad crossmatches.
One feature of ionospheric distortions which has not been fully exploited is that the distortions are spatially correlated (i.e. nearby sources are distorted in a similar way). Furthermore, since we now have good MWA catalogues over almost the whole sky, we can also use source brightness or morphology to find the optimum match for each source.
The project will therefore involve optimising and adding to our algorithms to allow many thousands of catalogues to be crossmatched automatically. Furthermore, when more than one crossmatch is feasible (or if there is a chance that a new source, not in the master catalogue, has been detected), then the probability of this should be robustly calculated.
The project would suit a student who is interested in solving problems on very large datasets. This is a very generic ‘big data’ problem and the skills learnt by the student will be extremely useful either inside or outside astronomy.
