Properties of Galaxy Dark Matter Halos from Weak Lensing
Bibliographic record
Abstract
We present the results of a study of weak lensing by galaxies based on 45.5 deg 2 of R C -band imaging data from the Red-Sequence Cluster Survey (RCS). We define a sample of lenses with 19.5 < R C < 21 and a sample of background galaxies with 21.5 < R C < 24. We present the first weak-lensing detection of the flattening of galaxy dark matter halos. We use a simple model in which the ellipticity of the halo is f times the observed ellipticity of the lens. We find a best-fit value of f = 0.77 , which suggests that the dark matter halos are somewhat rounder than the light distribution. The fact that we detect a significant flattening implies that the halos are well aligned with the light distribution. Given the average ellipticity of the lenses, this implies a halo ellipticity of ⟨ e halo ⟩ = 0.33 , in fair agreement with results from numerical simulations of cold dark matter. We note that this result is formally a lower limit to the flattening, since the measurements imply a larger flattening if the halos are not aligned with the light distribution. 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. We also study the average mass profile around the lenses, using a maximum likelihood analysis. We consider two models for the halo mass profile: a truncated isothermal sphere (TIS) and a Navarro-Frenk-White (NFW) profile. We adopt observationally motivated scaling relations between the lens luminosity and the velocity dispersion and the extent of the halo. The TIS model yields a best-fit velocity dispersion of σ = 136 ± 5 ± 3 km s -1 (all errors are 68% confidence limits; the first error bar indicates the statistical uncertainty, whereas the second error bar indicates the systematic error) and a truncation radius s = 185 h -1 kpc for a galaxy with a fiducial luminosity of L B = 10 10 h -2 L B ,☉ (under the assumption that the luminosity does not evolve with redshift). Alternatively, the best-fit NFW model yields a mass M 200 = (8.4 ± 0.7 ± 0.4) × 10 11 h -1 M ☉ and a scale radius r s = 16.2 h -1 kpc. This value for the scale radius is in excellent agreement with predictions from numerical simulations for a halo of this mass.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".