CARS: The CFHTLS-Archive-Research Survey II. Weighing dark matter halos of Lyman-break galaxies at z=3-5 ⋆
Bibliographic record
Abstract
Received; accepted Aims. We measure the clustering properties for a large samples of u- (z ∼ 3), g- (z ∼ 4), and r- (z ∼ 5) dropouts from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Deep fields. Methods. Photometric redshift distributions along with simulations allow us to de-project the angular correlation measurements and estimate physical quantities such as the correlation length, halo mass, galaxy bias, and halo occupation as a function of UV luminosity. Results. For the first time we detect a significant one-halo term in the correlation function at z ∼ 5. The comoving correlation lengths and halo masses of LBGs are found to decrease with decreasing rest-frame UV-luminosity. No significant redshift evolution is found in either quantity. The typical halo mass hosting an LBG is M> ∼ 10 12 h −1 M ⊙ and the halos are typically occupied by less than one galaxy. Clustering segregation with UV luminosity is clearly observed in the dropout samples, however redshift evolution cannot clearly be disentangled from systematic uncertainties introduced by the redshift distributions. We study a range of possible redshift distributions to illustrate the effect of this choice. Spectroscopy of representative subsamples is required to make high-accuracy
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".