Identifying anomalous radio sources in the Evolutionary Map of the Universe Pilot Survey using a complexity-based approach

Abstract

The Evolutionary Map of the Universe (EMU) large-area radio continuum survey will detect tens of millions of radio galaxies, giving an opportunity for the detection of previously unknown classes of objects. To maximise the scientific value and make new discoveries, the analysis of this data will need to go beyond simple visual inspection. We propose the coarse-grained complexity, a simple scalar quantity relating to the minimum description length of an image, that can be used to identify unusual structures. The complexity can be computed without reference to the broader sample or existing catalogue data, making the computation efficient on new surveys at very large scales (such as the full EMU survey). We apply our coarse-grained complexity measure to data from the EMU Pilot Survey to detect and confirm anomalous objects in this data set and produce an anomaly catalogue. Rather than work with existing catalogue data using a specific source detection algorithm, we perform a blind scan of the area, computing the complexity using a sliding square aperture. The effectiveness of the complexity measure for identifying anomalous objects is evaluated using crowd-sourced labels generated via the Zooniverse.org platform. We find that the complexity scan identifies unusual sources, such as odd radio circles, by partitioning on complexity. We achieve partitions where 5 per cent of the data is estimated to be 86 per cent complete, and 0.5 per cent is estimated to be 94 per cent pure, with respect to anomalies and use this to produce an anomaly catalogue.

Planetary Complexity Revealed by the Joint Differential Entropy of Eigencolors

“…For example, plumes, jets, and the active nucleus are all distinct parts of the same object (such as a radio galaxy), and observations of each part cannot be used to infer the distribution of the state space of the galaxy as a whole. This was the case in previous studies where we measured the coarsegrained complexity of images of extragalactic objects using an upper-bound approximation of the Kolmogorov Complexity instead of the entropy (Segal et al 2019(Segal et al , 2023.…”

Evolutionary Map of the Universe (EMU): A pilot search for diffuse, non-thermal radio emission in galaxy clusters with the Australian SKA Pathfinder

“…Gupta et al (2022) used unsupervised machine learning to highlight sources with peculiar morphologies, finding a handful of diffuse cluster sources in archival ASKAP data. Other methods include the combination of complexity metrics followed by crowd-sourced inspection of classified sources (Segal et al, 2023). Such techniques may be employed to search for diffuse cluster emission, which will be particularly useful when considering expanded cluster samples.…”