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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
of an area covered by the Dark Energy Survey, reaching a depth of 25–30
rms at a spatial resolution of
11–18 arcsec, resulting in a catalogue of
220 000 sources, of which
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 follow up on a report by Vacca et al. (2018) of 28 candidate large-scale diffuse synchrotron sources in an 8° × 8° area of the sky (centred at RA 5h0m0s; Dec 5°48ʹ00ʹʹ). These sources were originally observed at 1.4 GHz using a combination of the single-dish Sardinia Radio Telescope and archival NRAO VLA Sky Survey data. They are in an area with nine massive galaxy clusters at
$z \approx 0.1$
and are candidates for the first detection of filaments of the synchrotron cosmic web. We attempt to verify these candidate sources with lower frequency observations at 154 MHz with the Murchison Widefield Array and at 887 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP). We use a novel technique to calculate the surface brightness sensitivity of these instruments to show that our lower frequency observations, and in particular those by ASKAP, are ideally suited to detect large-scale, extended synchrotron emission. Nonetheless, we are forced to conclude that none of these sources are likely to be synchrotron in origin or associated with the cosmic web.
We describe an analysis of 3-GHz confusion-limited data from the Karl J. Jansky Very Large Array (VLA). We show that with minimal model assumptions, P(D), Bayesian and Markov-Chain Mone-Carlo (MCMC) methods can define the source count to levels some 10 times fainter than the conventional confusion limit. Our verification process includes a full realistic simulation that considers known information on source angular extent and clustering. It appears that careful analysis of the statistical properties of an image is more effective than counting individual objects.
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