We present the data and initial results from the first Pilot Survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers 270 sqdeg of an area cov
ered by the Dark Energy Survey, reaching a depth of 25--30 ujybm rms at a spatial resolution of $sim$ 11--18 arcsec, resulting in a catalogue of $sim$ 220,000 sources, of which $sim$ 180,000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface-brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.
We present the discovery of another Odd Radio Circle (ORC) with the Australian Square Kilometre Array Pathfinder (ASKAP) at 944 MHz. The observed radio ring, ORC J0102-2450, has a diameter of ~70 arcsec or 300 kpc, if associated with the central elli
ptical galaxy DES J010224.33-245039.5 (z ~ 0.27). Considering the overall radio morphology (circular ring and core) and lack of ring emission at non-radio wavelengths, we investigate if ORC J0102-2450 could be the relic lobe of a giant radio galaxy seen end-on or the result of a giant blast wave. We also explore possible interaction scenarios, for example, with the companion galaxy, DES J010226.15-245104.9, located in or projected onto the south-eastern part of the ring. We encourage the search for further ORCs in radio surveys to study their properties and origin.
There are two puzzles surrounding the Pleiades, or Seven Sisters. First, why are the mythological stories surrounding them, typically involving seven young girls being chased by a man associated with the constellation Orion, so similar in vastly sepa
rated cultures, such as the Australian Aboriginal cultures and Greek mythology? Second, why do most cultures call them Seven Sisters even though most people with good eyesight see only six stars? Here we show that both these puzzles may be explained by a combination of the great antiquity of the stories combined with the proper motion of the stars, and that these stories may predate the departure of most modern humans out of Africa around 100,000 BC.
Cross-matching catalogues from radio surveys to catalogues of sources at other wavelengths is extremely hard, because radio sources are often extended, often consist of several spatially separated components, and often no radio component is coinciden
t with the optical/infrared host galaxy. Traditionally, the cross-matching is done by eye, but this does not scale to the millions of radio sources expected from the next generation of radio surveys. We present an innovative automated procedure, using Bayesian hypothesis testing, that models trial radio-source morphologies with putative positions of the host galaxy. This new algorithm differs from an earlier version by allowing more complex radio source morphologies, and performing a simultaneous fit over a large field. We show that this technique performs well in an unsupervised mode.
We have found a class of circular radio objects in the Evolutionary Map of the Universe Pilot Survey, using the Australian Square Kilometre Array Pathfinder telescope. The objects appear in radio images as circular edge-brightened discs, about one ar
cmin diameter, that are unlike other objects previously reported in the literature. We explore several possible mechanisms that might cause these objects, but none seems to be a compelling explanation.
This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machin
e learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labelled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighbourhood similarity and K-means clustering of radio-astronomical features complexity. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features which can be applied to new radio survey data.
Future radio surveys will generate catalogues of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these
sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multi-wavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best with high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts.
We have completed a deep radio continuum survey covering 86 square degrees of the Spitzer-South Pole Telescope deep field to test whether bent-tail galaxies are associated with galaxy clusters. We present a new catalogue of 22 bent-tail galaxies and
a further 24 candidate bent-tail galaxies. Surprisingly, of the 8 bent-tail galaxies with photometric redshifts, only two are associated with known clusters. While the absence of bent-tail sources in known clusters may be explained by effects such as sensitivity, the absence of known clusters associated with most bent-tail galaxies casts doubt upon current models of bent-tail galaxies.
The volume of data that will be produced by the next generation of astrophysical instruments represents a significant opportunity for making unplanned and unexpected discoveries. Conversely, finding unexpected objects or phenomena within such large v
olumes of data presents a challenge that may best be solved using computational and statistical approaches. We present the application of a coarse-grained complexity measure for identifying interesting observations in large astronomical data sets. This measure, which has been termed apparent complexity, has been shown to model human intuition and perceptions of complexity. Apparent complexity is computationally efficient to derive and can be used to segment and identify interesting observations in very large data sets based on their morphological complexity. We show, using data from the Australia Telescope Large Area Survey, that apparent complexity can be combined with clustering methods to provide an automated process for distinguishing between images of galaxies which have been classified as having simple and complex morphologies. The approach generalizes well when applied to new data after being calibrated on a smaller data set, where it performs better than tested classification methods using pixel data. This generalizability positions apparent complexity as a suitable machine-learning feature for identifying complex observations with unanticipated features.
The extreme ULIRG F00183-7111 has recently been found to have a radio-loud AGN with jets in its centre, representing an extreme example of the class of radio-loud AGNs buried within dusty star-forming galaxies. This source appears to be a rare exampl
e of a ULIRG glimpsed in the (presumably) brief period as it changes from quasar mode to radio mode activity. Such transition stages probably account for many of the high-redshift radio-galaxies and extreme high-redshift ULIRGs, and so this object at the relatively low redshift of 0.328 offers a rare opportunity to study this class of objects in detail. We have also detected the CO signal from this galaxy with the ATCA, and here describe the implications of this detection for future ULIRG studies.
