THE COSMOS-WIRCam NEAR-INFRARED IMAGING SURVEY. I.<i>BzK</i>-SELECTED PASSIVE AND STAR-FORMING GALAXY CANDIDATES AT<i>z</i>≳ 1.4
Bibliographic record
Abstract
We present a new near-infrared survey covering the 2 deg 2 COSMOS field conducted using WIRCam at the Canada–France–Hawaii Telescope. By combining our near-infrared data with Subaru B and z images, we construct a deep, wide-field optical–infrared catalog. At K s < 23 (AB magnitudes), our survey completeness is greater than 90% and 70% for stars and galaxies, respectively, and contains 143,466 galaxies and 13,254 stars. Using the BzK diagram, we divide our galaxy catalog into quiescent and star-forming galaxy candidates. At z ∼ 2, our catalogs contain 3931 quiescent and 25,757 star-forming galaxies representing the largest and most secure sample at these depths and redshifts to date. Our counts of quiescent galaxies turns over at K s ∼ 22, an effect that we demonstrate cannot be due to sample incompleteness. Both the number of faint and bright quiescent objects in our catalogs exceed the predictions of a recent semi-analytic model of galaxy formation, indicating potentially the need for further refinements in the amount of merging and active galactic nucleus feedback at z ∼ 2 in these models. We measure the angular correlation function for each sample and find that the slope of the field galaxy correlation function flattens to 1.5 by K s ∼ 23. At small angular scales, the angular correlation function for passive BzK galaxies is considerably in excess of the clustering of dark matter. We use precise 30-band photometric redshifts to derive the spatial correlation length and the redshift distributions for each object class. At K s < 22, we find r γ/1.8 0 = 7.0 ± 0.5 h −1 Mpc for the passive BzK candidates and 4.7 ± 0.8 h −1 Mpc for the star-forming BzK galaxies. Our pBzK galaxies have an average photometric redshift of z p ∼ 1.4, in approximate agreement with the limited spectroscopic information currently available. The stacked K s image will be made publicly available from IRSA.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".