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Detecting complex sources in large surveys using an apparent complexity measure

Published online by Cambridge University Press:  01 August 2025

David Parkinson Open the ORCID record for David Parkinson [Opens in a new window]  and
Gary Segal
David Parkinson*
Affiliation:
Centre for Theoretical Astrophysics, Korea Astronomy and Space Science Institute, 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Republic of Korea
Gary Segal*
Affiliation:
School of Mathematics and Physics, University of Queensland, St Lucia, Brisbane, QLD 4072, Australia CSIRO Space and Astronomy, PO Box 76, Epping, 1710, NSW, Australia
*
email: DavidParkinson@kasi.re.kr
email: gp.segal@gmail.com
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Abstract

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.

Keywords

Extragalactic radio sourcesAstrostatisticsAstrostatistics tools

Information

Type
Contributed Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 80 - 85
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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