http://arxiv.org/abs/2212.00349
Large area astronomical surveys will almost certainly contain new objects of a type that have never been seen before. The detection of ‘unknown unknowns’ by an algorithm is a difficult problem to solve, as unusual things are often easier for a human to spot than a machine. We use the concept of apparent complexity, previously applied to detect multi-component radio sources, to scan the radio continuum Evolutionary Map of the Universe (EMU) Pilot Survey data for complex and interesting objects in a fully automated and blind manner. Here we describe how the complexity is defined and measured, how we applied it to the Pilot Survey data, and how we calibrated the completeness and purity of these interesting objects using a crowd-sourced ‘zoo’. The results are also compared to unexpected and unusual sources already detected in the EMU Pilot Survey, including Odd Radio Circles, that were found by human inspection.
D. Parkinson and G. Segal
Fri, 2 Dec 22
33/81
Comments: 6 pages, 4 figures. Prepared for the proceedings of the International Astronomical Union Symposium 368 “Machine Learning in Astronomy: Possibilities and Pitfalls”
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