Deep ASKAP EMU Survey of the GAMA23 field: properties of radio sources
Bibliographic record
Abstract
ABSTRACT We present the Australian Square Kilometre Array Pathfinder (ASKAP) observations of the Galaxy and Mass Assembly (GAMA)-23h field. The survey was carried out at 887.5 MHz and covers an ∼83 square deg field. We imaged the calibrated visibility data, taken as part of the Evolutionary Mapping of Universe Early Science Programme, using the latest version of the ASKAPSoft pipeline. The final mosaic has an angular resolution of 10 arcsec and a central rms noise of around 38 $\mu$Jy beam−1. The derived radio source catalogue has 39 812 entries above a peak flux density threshold of 5σ. We searched for the radio source host galaxy counterparts using the GAMA spectroscopic (with an i-band magnitude limit of 19.2 mag) and multiwavelength catalogues that are available as part of the collaboration. We identified hosts with GAMA spectroscopic redshifts for 5934 radio sources. We describe the data reduction, imaging, and source identification process, and present the source counts. Thanks to the wide area covered by our survey, we obtain very robust counts down to 0.2 mJy. ASKAP’s exceptional survey speed, providing efficient, sensitive, and high-resolution mapping of large regions of the sky in conjunction with the multiwavelength data available for the GAMA23 field, allowed us to discover 63 giant radio galaxies. The data presented here demonstrate the excellent capabilities of ASKAP in the pre-SKA era.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".