An Effective Cross-Matching Framework for Catalogues and Images
Stacking is a common task in astronomy that consists of combining images of the sky to gain an increased signal to noise. This technique can be very powerful, however it requires that the individual images are aligned correctly. At optical wavelengths, and high radio frequencies, this alignment process can be relatively easily achieved by applying a single shift/rotate to each of the images. However at low radio frequencies the ionosphere can cause lens-like distortions on scales smaller than the image. This effect combined with the often complicated structures of radio sources at low radio frequencies means that a single shift/rotate is no longer able to align the pixels in the image to produce a properly stacked image.
Another common task in astronomy consists of stacking catalogues together, in order to extract a light curve (flux vs time plot) for each source. The process of associating sources between different catalogues typically relies on a nearest-neighbour type matching scheme and thus requires catalogues to be aligned in order to reduce false matches. The process of aligning catalogues is the same as for images.
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.
Ultimately, the problem at hand is that of matching features within datasets. Whilst there are many domain-specific solutions, there is no general framework under which cross-matching can be achieved in the event of many-to-many matches. The aim of this project is to develop such a framework within which cross matching can be achieved in the general case where many-to-one and many-to-many matches are present. It is envisioned that the framework will be largely algorithmic, and presented as a set of software
When complete, this project will be able to demonstrate a method by which:
- Radio images can be stacked together to increase sensitivity, even in the presence of ionospheric distortions, and
- Catalogues of radio sources can be combined to produce consistent and meaningful light curves.