The 154 MHz radio sky observed by the Murchison Widefield Array: noise, confusion, and first source count analyses
Bibliographic record
Abstract
We analyse a 154 MHz image made from a 12 h observation with the Murchison Widefield Array (MWA) to determine the noise contribution and behaviour of the source counts down to 30 mJy. The MWA image has a bandwidth of 30.72 MHz, a field-of-view within the half-power contour of the primary beam of 570 deg2, a resolution of 2.3 arcmin and contains 13 458 sources above 5σ. The rms noise in the centre of the image is 4–5 mJy beam−1. The MWA counts are in excellent agreement with counts from other instruments and are the most precise ever derived in the flux density range 30–200 mJy due to the sky area covered. Using the deepest available source count data, we find that the MWA image is affected by sidelobe confusion noise at the ≈3.5 mJy beam−1 level, due to incompletely peeled and out-of-image sources, and classical confusion becomes apparent at ≈1.7 mJy beam−1. This work highlights that (i) further improvements in ionospheric calibration and deconvolution imaging techniques would be required to probe to the classical confusion limit and (ii) the shape of low-frequency source counts, including any flattening towards lower flux densities, must be determined from deeper ≈150 MHz surveys as it cannot be directly inferred from higher frequency data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".