Evolution in the bias of faint radio sources to z ∼ 2.2
Bibliographic record
Abstract
Quantifying how the baryonic matter traces the underlying dark matter distribution is key to both understanding galaxy formation and our ability to constrain the cosmological model. Using the cross-correlation function of radio and near-infrared galaxies, we present a large-scale clustering analysis of radio galaxies to z ∼ 2.2. We measure the angular auto-correlation function of Ks<23.5 galaxies in the VISTA Deep Extragalactic Observations (VIDEO-XMM3) field with photometric redshifts out to z = 4 using VIDEO and CFHTLS (Canada–France–Hawaii Telescope Legacy Survey) photometry in the near-infrared and optical. We then use the cross-correlation function of these sources with 766 radio sources at S1.4>90μJy to infer linear bias of radio galaxies in four redshift bins. We find that the bias evolves from b = 0.57 ± 0.06 at z ∼ 0.3 to 8.55 ± 3.11 at z ∼ 2.2. Furthermore, we separate the radio sources into subsamples to determine how the bias is dependent on the radio luminosity, and find a bias which is significantly higher than predicted by the simulations of Wilman et al., and consistent with the lower luminosity but more abundant FR-I population having a similar bias to the highly luminous but rare FR-IIs. Our results are suggestive of a higher mass, particularly for FR-I sources than assumed in simulations, especially towards higher redshift.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".