Properties of galaxy dark matter halos from weak lensing
Bibliographic record
Abstract
We present the results of a study of the average mass profile around galaxies using weak gravitational lensing. We use 45.5 deg2 of RC band imaging data from the Red-Sequence Cluster Survey (RCS) and define a sample of ~ 1.2 × 105 lenses with 19.5 < RC < 21, and a sample of ~ 1.5 × 106 background galaxies with 21.5 < R < 24. We constrain the power law scaling relations between the B-band luminosity and the mass and size of the halo, and find that the results are in excellent agreement with observed luminosity–line-width relations. Under the assumption that the luminosity does not evolve with redshift, the best fit NFW model yields a mass M200 = (8.8±0.7) × 1011h–1M⊙ and a scale radius rs = I6.7+3.7–3.0h–1 kpc for a galaxy with a fiducial luminosity of Lb = 1010h–2LB⊙. the latter result is in excellent agreement with predictions from numerical simulations for a halo of this mass. We also observe a signficant anisotropy of the lensing signal around the lenses, implying that the halos are flattened and aligned with the light distribution. We find an average (projected) halo ellipticity of 〈ehalo〉 = 0.20+0.04–0.05, in fair agreement with results from numerical simulations of CDM. Alternative theories of gravity (without dark matter) predict an isotropic lensing signal, which is excluded with 99.5% confidence. Hence, our results provide strong support for the existence of dark matter.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".