The deep diffuse extragalactic radio sky at 1.75 GHz
Bibliographic record
Abstract
We present a study of diffuse extragalactic radio emission at 1.75 GHz from part of the ELAIS-S1 (European Large Area Infrared Space Observatory Survey – South 1) field using the Australia Telescope Compact Array. The resulting mosaic is 2.46 deg2, with a roughly constant noise region of 0.61 deg2 used for analysis. The image has a beam size of 150 arcsec × 60 arcsec and instrumental 〈σn〉 = (52 ± 5) μJy beam−1. Using point-source models from the Australia Telescope Large Area Survey, we subtract the discrete emission in this field for S ≥ 150 μJy beam−1. Comparison of the source-subtracted probability distribution, or P(D), with the predicted distribution from unsubtracted discrete emission and noise, yields an excess of (76 ± 23) μJy beam−1. Taking this as an upper limit on any extended emission, we constrain several models of extended source counts, assuming Ωsource ≤ 2 arcmin. The best-fitting models yield temperatures of the radio background from extended emission of Tb = (10 ± 7) mK, giving an upper limit on the total temperature at 1.75 GHz of (73 ± 10) mK. Further modelling shows that our data are inconsistent with the reported excess temperature of ARCADE2 to a source-count limit of 1 μJy. Our new data close a loop-hole in the previous constraints, because of the possibility of extended emission being resolved out at higher resolution. Additionally, we look at a model of cluster halo emission and two dark matter particle annihilation source-count models, and discuss general constraints on any predicted counts from such sources. Finally, we report the derived integral count at 1.4 GHz using the deepest discrete count plus our new extended-emission limits, providing numbers that can be used for planning future ultradeep surveys.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".