An enhanced abundance of bright galaxies in protocluster candidates at z ∼ 3–5
Bibliographic record
Abstract
ABSTRACT We present a protocluster search covering z ∼ 3 to z ∼ 5 based on the combination of the Hyper SuprimeCam Subaru Strategic Programme and the CFHT Large Area U-band Deep Survey. We identify about 30 protocluster candidates per unit redshift over the $\sim 25\, \mathrm{deg^2}$ area of the Deep/UltraDeep layer. Protocluster candidates are selected as regions with a significantly enhanced surface density of dropout galaxies. With this large sample, we characterize the properties of their individual member galaxies. We compare the number counts of dropout galaxies in protocluster candidates with that of coeval field galaxies. Rest-frame ultraviolet (UV) bright galaxies are overabundant in protocluster candidates, a trend seen across the full redshift range studied. We do not find evidence for their spatial distribution within protocluster candidates to be distinct from their fainter counterparts, nor for their UV colour to be different from that of field galaxies with the same brightness. Cosmological simulations predict this bright-end excess, with the main cause being a richer population of massive galaxies, with only a minor contribution from an enhancement in star formation activity (and therefore UV emission) at fixed mass. U-to-K SED modelling of our observed samples supports this interpretation. This environmental differentiation in number counts is already in place at z ∼ 5, with no significant redshift dependence over the range in lookback times probed . These observational results and model predictions suggest that the cosmic clock is ahead in high-density environments.
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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 | 0.001 | 0.000 |
| 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) | 0.003 | 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".