The Galaxy–Halo Connection in High-redshift Universe: Details and Evolution of Stellar-to-halo Mass Ratios of Lyman Break Galaxies on CFHTLS Deep Fields
Bibliographic record
Abstract
Abstract We present the results of clustering analyses of Lyman break galaxies (LBGs) at , 4, and 5 using the final data release of the Canada–France–Hawaii Telescope Legacy Survey (CFHTLS). Deep- and wide-field images of the CFHTLS Deep Survey enable us to obtain sufficiently accurate two-point angular correlation functions to apply a halo occupation distribution analysis. The mean halo masses, calculated as , increase with the stellar-mass limit of LBGs. The threshold halo mass to have a central galaxy, follows the same increasing trend as the low-z results, whereas the threshold halo mass to have a satellite galaxy, M 1, shows higher values at than , over the entire stellar mass range. Satellite fractions of dropout galaxies, even at less massive halos, are found to drop sharply, from z = 2 down to less than 0.04, at . These results suggest that satellite galaxies form inefficiently within dark halos at , even for less massive satellites with . We compute stellar-to-halo mass ratios (SHMRs) assuming a main sequence of galaxies, which is found to provide SHMRs consistent with those derived from a spectral energy distribution fitting method. The observed SHMRs are in good agreement with model predictions based on the abundance-matching method, within confidence intervals. We derive observationally, for the first time, , which is the halo mass at a peak in the star-formation efficiency, at , and it shows a small increasing trend with cosmic time at . In addition, and its normalization are found to be almost unchanged during . Our study provides observational evidence that galaxy formation is ubiquitously most efficient near a halo mass of over cosmic 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 | 0.000 | 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 | 0.000 | 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".