LARgE Survey – I. Dead monsters: the massive end of the passive galaxy stellar mass function at cosmic noon
Bibliographic record
Abstract
We introduce the largest to date survey of massive quiescent galaxies at redshift z ∼ 1.6. With these data, which cover 27.6 deg2, we can find significant numbers of very rare objects such as ultra-massive quiescent galaxies that populate the extreme massive end of the galaxy mass function, or dense environments that are likely to become present-day massive galaxy clusters. In this paper, the first in a series, we apply our gzKs adaptation of the BzK technique to select our z ∼ 1.6 galaxy catalogue and then study the quiescent galaxy stellar mass function with good statistics over M⋆ ∼ 1010.2–1011.7 M⊙ – a factor of 30 in mass – including 60 ultra-massive z ∼ 1.6 quiescent galaxies with M⋆ > 1011.5 M⊙. We find that the stellar mass function of quiescent galaxies at z ∼ 1.6 is well represented by the Schechter function over this large mass range. This suggests that the mass-quenching mechanism observed at lower redshifts must have already been well established by this epoch, and that it is likely due to a single physical mechanism over a wide range of mass. This close adherence to the Schechter shape also suggests that neither merging nor gravitational lensing significantly affects the observed quenched population. Finally, comparing measurements of |$M^\ast$| parameters for quiescent and star-forming populations (ours and from the literature), we find hints of an offset (|$M^\ast _{\mathrm{ SF}}\gt M^\ast _{\mathrm{ PE}}$|), which could suggest that the efficiency of the quenching process evolves with time.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".