Preprint typeset using L ATEX style emulateapj v. 10/09/06 THE MASSES AND SHAPES OF DARK MATTER HALOS FROM GALAXY-GALAXY LENSING IN THE
Bibliographic record
Abstract
We present the first galaxy-galaxy weak lensing results using early data from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). These results are based on ∼22 deg 2 of i ′ data. From this data, we estimate the average velocity dispersion for an L * galaxy at a redshift of 0.3 to be 137 ± 11 km s −1, with a virial mass, M200, of 1.1 ± 0.2 ×10 12 h −1 M ⊙ and a rest frame mass-to-light ratio of 173±34 hM⊙/LRc⊙. We also investigate various possible sources of systematic error in detail. Additionally, we separate our lens sample into two sub-samples, divided by apparent magnitude, thus average redshift. From this early data we do not detect significant evolution in galaxy dark matter halo mass-to-light ratios from a redshift of 0.45 to 0.27. Finally, we test for non-spherical galaxy dark matter halos. Our results favor a dark matter halo with an ellipticity of ∼0.3 at the 2 σ level when averaged over all galaxies. If the sample of foreground lens galaxies is selected to favor ellipticals, the mean halo ellipticity and significance of this result increase. Subject headings: gravitational lensing, dark matter, mass-to-light ratios, galaxy halos, halo shapes 1.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.575 | 0.426 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".