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13th of April 2023
Identifying anomalous radio sources in the EMU Pilot Survey using a complexity-based approach
by Segal et al.
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. Segal et al. apply a coarse-grained complexity measure to data from the EMU Pilot Survey to detect, confirm the presence of anomalous objects in this data set. The coarse-grained complexity is 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 Rather than work with existing catalogue data using a specific source detection algorithm, they 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. They find that the complexity scan identifies unusual sources, such as odd radio circles, by partitioning on complexity.

The figure above shows examples of objects found within frames sampled above the 99th percentile complexity value from the EMU-PS Complexity Scan. While a number of these had previously been identified, the re-detection provides confidence in the technique. These examples illustrate the breadth of objects found within frames in the complexity tail including, (e) the face-on spiral galaxy NGC 7125, (f) an FR-II (Fanaroff-Riley class II) remnant, (g) an FR-I (Fanaroff-Riley class I) potential Double-double radio galaxy, and (h) an FR-I extended radio source with host galaxy. (Examples (a) to (d) were featured in Tuesday's ADAP.)

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