Supervisor: Dr Megan Argo
Radio telescope observatories are increasingly suffering from radio pollution in a similar way to optical telescopes suffering from light pollution. The proliferation of mobile phone networks, as well as the surge in constellations of satellites from SpaceX, OneWeb, Amazon and others, is making the radio environment more and more challenging for ground-based radio astronomy everywhere on the Earth’s surface. Recent progress has been made in applying machine learning techniques to the problem by recognising interference in spectrograms from the UK’s e-MERLIN interferometer and generating flag masks to excise affected data, but there is much more that can be explored. Students interested in this problem could work on extending machine learning methods for interference recognition, looking at the characteristics of radio interference from various sources, including predicted satellite constellations, and exploring methods of dealing with the problem in real astronomical data.