Detecting complex sources in large surveys using an apparent complexity measure

TL;DR

This paper introduces an automated method using apparent complexity to detect unusual and complex astronomical objects in large radio surveys, aiming to identify new phenomena without human bias.

Contribution

It applies the concept of apparent complexity to radio survey data for the first time, enabling blind detection of complex sources and calibration with crowd-sourced validation.

Findings

Successfully identified complex sources in EMU Pilot Survey

Calibrated detection performance with crowd-sourced data

Detected known unusual objects like Odd Radio Circles

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.