The faint and extremely red K-band-selected galaxy population in the DEEP2/Palomar fields
Bibliographic record
Abstract
We present in this paper an analysis of the faint and red near-infrared (NIR) selected galaxy population found in NIR imaging from the Palomar Observatory Wide-Field Infrared Survey. This survey covers 1.53 deg2 to 5σ detection limits of Kvega= 20.5–21 and Jvega= 22.5, and overlaps with the DEEP2 spectroscopic redshift survey. We discuss the details of this NIR survey, including our J- and K-band counts. We show that the K-band galaxy population has a redshift distribution that varies with K magnitude, with most K < 17 galaxies at z < 1.5 and a significant fraction (38.3 ± 0.3 per cent) of K > 19 systems at z > 1.5. We further investigate the stellar masses and morphological properties of K-selected galaxies, particularly extremely red objects (EROs), as defined by (R−K) > 5.3 and (I−K) > 4. One of our conclusions is that the ERO selection is a good method for picking out galaxies at z > 1.2, and within our magnitude limits, the most massive galaxies at these redshifts. The ERO limit finds 75 per cent of all M* > 1011M⊙ galaxies at z∼ 1.5 down to Kvega= 19.7. We further find that the morphological breakdown of K < 19.7 EROs is dominated by early-types (57 ± 3 per cent) and peculiars (34 ± 3 per cent). However, about a fourth of the early-types are distorted ellipticals, and within CAS (concentration, asymmetry, clumpiness) parameter space these bridge the early-type and peculiar population, suggesting a morphological evolutionary sequence. We also investigate the use of a (I−K) > 4 selection to locate EROs, finding that it selects galaxies at slightly higher average redshifts (〈z〉= 1.43 ± 0.32) than the (R−K) > 5.3 limit with 〈z〉= 1.28 ± 0.23. Finally, by using the redshift distribution of K < 20 selected galaxies, and the properties of our EROs, we are able to rule out all monolithic collapse models for the formation of massive galaxies.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".