The clustering and abundance of star-forming and passive galaxies at<i>z</i>∼ 2
Bibliographic record
Abstract
We use the UKIRT (United Kingdom Infrared Telescope) Infrared Deep Sky Survey (UKIDSS) Ultra-deep survey (UDS), currently the deepest panoramic near-infrared survey, together with deep Subaru optical imaging to measure the clustering, number counts and luminosity function of galaxies at z∼ 2 selected using the BzK selection technique. We find that both star-forming (sBzK) and passive (pBzK) galaxies, to a magnitude limit of KAB < 23, are strongly clustered. The passive galaxies are the most strongly clustered population, with scalelengths of r0= 15.0+1.9−2.2h−1Mpc compared with r0= 6.75+0.34−0.37h−1Mpc for star-forming galaxies. The direct implication is that passive galaxies inhabit the most massive dark matter haloes, and are thus identified as the progenitors of the most massive galaxies at the present day. In addition, the pBzKs exhibit a sharp flattening and potential turnover in their number counts, in agreement with other recent studies. This plateau cannot be explained by the effects of incompleteness. We conclude that only very massive galaxies are undergoing passive evolution at this early epoch, consistent with the downsizing scenario for galaxy evolution. Assuming a purely passive evolution for the pBzKs from their median redshift to the present day, their luminosity function suggests that only ∼2.5 per cent of present-day massive ellipticals had a pBzK as a main progenitor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".