The Australia Telescope Compact Array Broad-band Backend: description and first results★
Bibliographic record
Abstract
Here, we describe the Compact Array Broad-band Backend (CABB) and present first results obtained with the upgraded Australia Telescope Compact Array (ATCA). The 16-fold increase in observing bandwidth, from 2 × 128 to 2 × 2048 MHz, high-bit sampling and the addition of 16 zoom windows (each divided into further 2048 channels) provide major improvements for all ATCA observations. The benefits of the new system are: (1) hugely increased radio continuum and polarization sensitivity as well as image fidelity; (2) substantially improved capability to search for and map emission and absorption lines over large velocity ranges; (3) simultaneous multi-line and continuum observations; (4) increased sensitivity, survey speed and dynamic range due to high-bit sampling and (5) high-velocity resolution, while maintaining full polarization output. The new CABB system encourages all observers to make use of both spectral line and continuum data to achieve their full potential. Given the dramatic increase of the ATCA capabilities in all bands (ranging from 1.1 to 105 GHz) CABB enables scientific projects that were not feasible before the upgrade, such as simultaneous observations of multiple spectral lines, on-the-fly mapping, fast follow-up of radio transients (e.g. the radio afterglow of new supernovae) and maser observation at high-velocity resolution and full polarization. The first science results presented here include wide-band spectra, high dynamic-range images and polarization measurements, highlighting the increased capability and discovery potential of the ATCA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".