Resolving Source Confusion in the MIGHTEE XMM-LSS Field using XID+

Project Description:

This project focuses on overcoming source confusion in deep radio surveys. Radio telescopes like MeerKAT can detect extremely faint signals from star-forming galaxies and active galactic nuclei. However, at these faint limits, multiple galaxies often blend together within the telescope beam, making it difficult to isolate individual objects accurately. To solve this, the student will apply a Bayesian probabilistic deblending tool, XID+, to the MIGHTEE XMM-LSS field. The process begins by using unsupervised machine learning to identify and remove stars from the dataset, as they are unlikely to produce detectable radio flux. Following this, the student will use stellar mass to select the remaining galaxies that are most likely to be radio emitters. Since stellar mass correlates strongly with star formation rates, this method is a highly efficient way to build a target list for the deblending software. The primary outcome will be an accurate measurement of faint radio source counts well below the traditional confusion limit.

Research Area:

Astronomy

Project Level:

Masters

This Project Is Offered At The Following Node(s):

(UCT)(UKZN)(NWU)

Special Requirements:

The student should have basic programming skills in Python or other languages