KECK DEEP FIELDS. IV. LUMINOSITY-DEPENDENT CLUSTERING AND GALAXY DOWNSIZING IN UV-SELECTED GALAXIES AT<i>z</i>= 4, 3, AND 2
Bibliographic record
Abstract
We investigate the luminosity-dependent clustering of rest-frame UV-selected galaxies at z ∼ 4, 3, 2.2, and 1.7 in the Keck Deep Fields, which are complete to = 27 and cover 169 arcmin 2 . We find that at z ∼ 4 and 3, UV-bright galaxies cluster more strongly than UV-faint ones, but at z ∼ 2.2 and 1.7, the UV-bright galaxies are no longer the most strongly clustered. We derive mass estimates for objects in our sample by comparing our measurements to the predicted clustering of dark matter halos in the Millennium Simulation. From these estimates, we infer relationships between halo mass and star formation rate (SFR), and find that the most massive dark matter halos in our sample host galaxies with high SFRs ( M 1700 < −20, or >50 M ☉ yr −1 ) at z ∼ 3 and 4, moderate SFRs (−20 < M 1700 < −19, or ∼20 M ☉ yr −1 ) at z ∼ 2.2, and lower SFRs (−19 < M 1700 < −18, or ∼2 M ☉ yr −1 ) at z ∼ 1.7. We believe our measurements may provide a new line of evidence for galaxy downsizing by extending that concept from stellar to halo mass. We also find that the objects with blue UV colors in our sample are much more strongly clustered than those with red UV colors, and we propose that this may be due to the presence of the 2175 Å dust absorption bump in more massive halos, which contain the older stellar populations and dust needed to produce the feature. The relatively small area covered by the survey means that the absolute values of the correlation lengths and halo masses we derive are heavily dependent on the "integral constraint" correction, but the uniformly deep coverage across a large-redshift interval allows us to detect several important trends that are independent of this correction.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".