Clustering properties of a type-selected volume-limited sample of galaxies in the CFHTLS
Bibliographic record
Abstract
We present an investigation of the clustering of the faint () field galaxy population in the redshift range . Using 100 000 precise photometric redshifts extracted from galaxies in the four ultra-deep fields of the Canada-France Legacy Survey, we construct a set of volume-limited galaxy samples. We use these catalogues to study in detail the dependence of the amplitude Aw and slope δ of the galaxy correlation function w on absolute MB rest-frame luminosity, redshift, and best-fitting spectral type (or, equivalently, rest-frame colour). Our derived comoving correlation lengths for magnitude-limited samples are in excellent agreement with measurements made in spectroscopic surveys. Our main conclusions are as follows: 1. the comoving correlation length for all galaxies with declines steadily from to ; 2. at all redshifts and luminosity ranges, galaxies with redder rest-frame colours have clustering amplitudes from two and three times higher than bluer ones; 3. for both the red and blue galaxy populations, the clustering amplitude is invariant with redshift for bright galaxies (); 4. at for less luminous galaxies with we find higher clustering amplitudes of ~ Mpc; 5. the relative bias between redder and bluer rest-frame populations increases gradually towards fainter magnitudes. Among the most important implications of these results is that although the full galaxy population traces the underlying dark matter distribution quite well (and is therefore quite weakly biased), redder, older galaxies have clustering lengths that are almost invariant with redshift are quite strongly biased by .
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".