The VIPERS Multi-Lambda Survey
Bibliographic record
Abstract
We investigate the evolution of the galaxy stellar mass function and stellar mass density from redshift z = 0.2 to z = 1.5 of a Ks < 22-selected sample with highly reliable photometric redshifts and over an unprecedentedly large area. Our study is based on near-infrared observations carried out with the WIRCam instrument at CFHT over the footprint of the VIPERS spectroscopic survey and benefits from the high-quality optical photometry from the CFHTLS and ultraviolet observations with the GALEX satellite. The accuracy of our photometric redshifts is σΔz/ (1 + z) < 0.03 and 0.05 for the bright (iAB< 22.5) and faint (iAB > 22.5) samples, respectively. The galaxy stellar mass function is measured with ~760 000 galaxies down to Ks ~ 22 and over an effective area of ~22.4 deg2, the latter of which drastically reduces the statistical uncertainties (i.e. Poissonian error and cosmic variance). We point out the importance of carefully controlling the photometric calibration, whose effect becomes quickly dominant when statistical uncertainties are reduced, which will be a major issue for future cosmological surveys with EUCLID or LSST, for instance. By exploring the rest-frame (NUV−r) vs. (r−Ks) colour-colour diagram with which we separated star-forming and quiescent galaxies, (1) we find that the density of very massive log (M∗/M⊙) > 11.5 galaxies is largely dominated by quiescent galaxies and increases by a factor 2 from z ~ 1 to z ~ 0.2, which allows for additional mass assembly through dry mergers. (2) We also confirm the scenario in which star formation activity is impeded above a stellar mass log(ℳ*SF/M⊙) = 10.64±0.01. This value is found to be very stable at 0.2 <z< 1.5. (3) We discuss the existence of a main quenching channel that is followed by massive star-forming galaxies, and we finally (4) characterise another quenching mechanism that is required to explain the clear excess of low-mass quiescent galaxies that is observed at low redshift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".