New constraints on the 1.4 GHz source number counts and luminosity functions in the Lockman Hole field
Bibliographic record
Abstract
ABSTRACT We present a study of the 1173 sources brighter than $S_{1.4\, \rm GHz}= 120\, \mu$Jy detected over an area of $\simeq 1.4\, \hbox{deg}^{2}$ in the Lockman Hole field. Exploiting the multiband information available in this field for ∼79 per cent of the sample, sources have been classified into radio loud (RL) active galactic nuclei (AGNs), star-forming galaxies (SFGs), and radio quiet (RQ) AGNs, using a variety of diagnostics available in the literature. Exploiting the observed tight anticorrelations between IRAC band 1 or band 2 and the source redshift we could assign a redshift to 177 sources missing a spectroscopic measurement or a reliable photometric estimate. A Monte Carlo approach was used to take into account the spread around the mean relation. The derived differential number counts and luminosity functions at several redshifts of each population show a good consistency with models and with earlier estimates made using data from different surveys and applying different approaches. Our results confirm that below $\sim 300\, \mu$Jy SFGs+RQ AGNs overtake RL AGNs that dominate at brighter flux densities. We also confirm earlier indications of a similar evolution of RQ AGNs and SFGs. Finally, we discuss the angular correlation function of our sources and highlight its sensitivity to the criteria used for the classification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".