J/other/PASA/42.13 Complex sources in RACS-Low DR1 (Alam+, 2025)
A catalogue of complex radio sources in the Rapid ASKAP Continuum Survey created
using a self-organising map.
Alam A., Pimbblet K.A., Gordon Y.A.
<Publ. Astron. Soc. Australia 42, 13 (2025)>
=2025PASA...42...13A 2025PASA...42...13A (SIMBAD/NED BibCode)
ADC_Keywords: Galaxies, radio ; Radio sources ; Radio continuum ; Morphology
Keywords: radio continuum: galaxies - methods: data analysis - catalogues
Abstract:
Next generations of radio surveys are expected to identify tens of
millions of new sources, and identifying and classifying their
morphologies will require novel and more efficient methods.
Self-Organising Maps (SOMs), a type of unsupervised machine learning,
can be used to address this problem. We map 251259 multi-Gaussian
sources from Rapid ASKAP Continuum Survey (RACS) onto a SOM with
discrete neurons. Similarity metrics, such as Euclidean distances, can
be used to identify the best-matching neuron or unit (BMU) for each
input image. We establish a reliability threshold by visually
inspecting a subset of input images and their corresponding BMU. We
label the individual neurons based on observed morphologies and these
labels are included in our value-added catalogue of RACS sources.
Sources for which the Euclidean distance to their BMU is ≃5
(accounting for approximately 79% of sources) have an estimated >90%
reliability for their SOM-derived morphological labels. This
reliability falls to less than 70% at Euclidean distances ≥7.
Beyond this threshold it is unlikely that the morphological label will
accurately describe a given source. Our catalogue of complex radio
sources from RACS with their SOM-derived morphological labels from
this work will be made publicly available.
Description:
This catalogue provides morphological classifications for complex
radio sources from RACS-Low DR1 created using a SOM, along with
SOM-derived metrics such as BMU, Euclidean distance. We also include
the reliability threshold based on our validation process.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
somracs.dat 113 251259 Catalogue of complex radio sources
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See also:
J/other/PASA/38.58 : Rapid ASKAP Continuum Survey. II. RACS cat. (Hale+, 2021)
Byte-by-byte Description of file: somracs.dat
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Bytes Format Units Label Explanations
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1- 25 A25 --- Name Source name from RACS-Low,
RACS-DR1 JHHMMSS.s+DDMMSS (1)
27- 45 A19 --- ID Source identifier from RACS-Low,
RACSHHMM+NNANNNN (1)
47- 56 F10.6 deg RAdeg Right Ascension (ICRS) from RACS-Low (1)
58- 67 F10.6 deg DEdeg Declination (ICRS) from RACS-Low (1)
69- 74 A6 --- bmu Position of the best matching unit or neuron
in the SOM grid in the format (y, x) (2)
76- 87 F12.8 --- DistEucl Euclidean distance to the BMU (2)
89-107 A19 --- MorpLabel Morphological label based on the visual
inspection of the individual neurons
in the SOM grid (3)
109-113 F5.2 % Match Reliability (in percentage) based on the
validation process (4)
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Note (1): Parent survey: RACS-Low DR1 (McConnell et al., 2020PASA...37E..48M 2020PASA...37E..48M,
Hale et al., 2021PASA...47E..58H 2021PASA...47E..58H)
The source identifiers (Name, ID) and positions are taken from the published
RACS-Low DR1 catalogue
(https://research.csiro.au/racs/home/data-2/racs-low-dr1-data/).
Note (2): The bmu and DistEucl are SOM-derived metrics.
Note (3): The morphological annotation and validation process gives rise to the
morphological_label (MorpLabel) and match percent (Match).
Further details can be found in Section 4 of the paper.
Note (4): Morphological labels are compact, extended compact, connected double,
split double, triple and uncertain/ambiguous.
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Acknowledgements:
From Afrida Alam, a.alam-2019(at)hull.ac.uk
This catalogue makes use of RACS-Low DR1 catalogue from the CSIRO
ASKAP Science Data Archive (CASDA). Morphological classification was
added by Alam et al., 2025PASA...42E..13A 2025PASA...42E..13A.
Our research made use of Parallelized rotation and flipping INvariant
Kohonen-maps or PINK (Polsterer et al., 2015ASPC..495...81P 2015ASPC..495...81P) as well
as the python package PYINK (https://github.com/tjgalvin/pyink).
References:
Alam et al., 2025PASA...42E..13A 2025PASA...42E..13A A catalogue of complex radio sources
in the Rapid ASKAP Continuum Survey created using a self-organising map
McConnell et al., 2020PASA...37E..48M 2020PASA...37E..48M The Rapid ASKAP Continuum Survey
I: Design and first results
Hale et al., 2021PASA...38E..58H 2021PASA...38E..58H The Rapid ASKAP Continuum Survey
II: First Stokes I Source Catalogue Data Release
Polsterer et al., 2015ASPC..495...81P 2015ASPC..495...81P Automatic galaxy classification via
machine learning techniques: Parallelized rotation/flipping INvariant
Kohonen maps (PINK)
(End) Afrida Alam [University of Hull], Patricia Vannier [CDS] 17-Sep-2025