RESOLVING THE RADIO SOURCE BACKGROUND: DEEPER UNDERSTANDING THROUGH CONFUSION
Bibliographic record
Abstract
We used the Karl G. Jansky Very Large Array to image one primary beam area at 3 GHz with 8'' FWHM resolution and 1.0 μJy beam −1 rms noise near the pointing center. The P ( D ) distribution from the central 10 arcmin of this confusion-limited image constrains the count of discrete sources in the 1 < S (μJy) < 10 range. At this level, the brightness-weighted differential count S 2 n ( S ) is converging rapidly, as predicted by evolutionary models in which the faintest radio sources are star-forming galaxies; and ≈96% of the background originating in galaxies has been resolved into discrete sources. About 63% of the radio background is produced by active galactic nuclei (AGNs), and the remaining 37% comes from star-forming galaxies that obey the far-infrared (FIR)/radio correlation and account for most of the FIR background at λ ≈ 160 μm. Our new data confirm that radio sources powered by AGNs and star formation evolve at about the same rate, a result consistent with AGN feedback and the rough correlation of black hole and stellar masses. The confusion at centimeter wavelengths is low enough that neither the planned Square Kilometre Array nor its pathfinder ASKAP EMU survey should be confusion limited, and the ultimate source detection limit imposed by "natural" confusion is ⩽0.01 μJy at ν = 1.4 GHz. If discrete sources dominate the bright extragalactic background reported by ARCADE 2 at 3.3 GHz, they cannot be located in or near galaxies and most are ⩽0.03 μJy at 1.4 GHz.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".