The numbers of z ∼ 2 star-forming and passive galaxies in 2.5 square degrees of deep CFHT imaging
Bibliographic record
Abstract
We use an adaptation of the BzKs technique to select ∼40 000 z ∼ 2 galaxies (to KAB = 24), including ∼5000 passively evolving (PE) objects (to KAB = 23), from 2.5 deg2 of deep Canada–France–Hawaii Telescope (CFHT) imaging. The passive galaxy luminosity function (LF) exhibits a clear peak at R = 22 and a declining faint-end slope (|$\alpha = -0.12 ^{+0.16}_{-0.14}$|), while that of star-forming galaxies is characterized by a steep faint-end slope [|$\alpha = -1.43\pm 0.02({\rm systematic})^{+0.05}_{-0.04}({\rm random})$|]. The details of the LFs are somewhat sensitive (at the <25 per cent level) to cosmic variance even in these large (∼0.5 deg2) fields, with the D2 field (located in the Cosmological Evolution Survey, COSMOS field) most discrepant from the mean. The shape of the z ∼ 2 stellar mass function of passive galaxies is remarkably similar to that at z ∼ 0.9, save for a factor of ∼4 lower number density. This similarity suggests that the same mechanism may be responsible for the formation of passive galaxies seen at both these epochs. This same formation mechanism may also operate down to z ∼ 0 if the local PE galaxy mass function, known to be two-component, contains two distinct galaxy populations. This scenario is qualitatively in agreement with recent phenomenological mass-quenching models and extends them to span more than three quarters of the history of the Universe.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".