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This past spring, Dunlap Institute Fellow Dr. Luke Pratley was awarded the Michael Penston RAS Thesis Prize and the IAU PhD prize for his PhD thesis, ”Radio Astronomy Image Reconstruction in the Big Data Era.”
His passion for astronomy started with a love of math and physics. As he continued through graduate studies, he found his passion for astronomical research and radio astronomy.
The team at Dunlap caught up with Pratley (remotely!) to learn more about his amazing work.
I first became interested in Physics and Astronomy during high school. I wasn’t a gifted student, but I figured I had nothing to lose by spending my time on something meaningful. I spent a lot of time at university learning the basics and making sure I would learn how to get to the right answer after getting it wrong. After you start to know the basics intuitively, everything is just a bit extra on top. This set a solid foundation of mathematics and physics, which is critical for research in astrophysics.
I got the opportunity to try research in astrophysics during my undergraduate studies in New Zealand. This was where I found that it was exciting to be able to take skills and subjects that I was learning to make a new discovery about the Universe. The idea of nothing to lose gave me the freedom to explore new ideas. I found being able to connect the dots between different areas of research is what I enjoy most. I then did my MSc in Theoretical Condensed Matter physics, which I enjoyed but I felt there were more opportunities for me in astronomy. This lead to my PhD at University College London, where I could apply the mathematics of new signal reconstruction methods to radio telescopes. My undergraduate experience was critical, because I could take my knowledge of radio data and bridge the gap with new mathematical techniques.
You won two prestigious awards for your thesis – can you summarize that work for us non-astronomers?
My PhD thesis showed state of the art signal reconstruction methods can be distributed over supercomputers, to create more detailed images of the radio sky. An important result was that I developed algorithms to efficiently correct and model telescope defects over ultra wide-fields of view in a way that was never expected or possible in a practical setting. Methods like this are needed to make new discoveries from new radio telescopes.
Tell us a bit about your current research.
What’s next for you?
My five-year plan is to develop and apply new signal processing techniques, to unlock new discoveries about the Universe.