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 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 $f=0.77^{+0.18}_{-0.21}$, suggesting 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 $<e_{\rm halo} >=0.33^{+0.07}_{-0.09}$, in fair agreement with results from numerical simulations of CDM. This result provides strong support for the existence of dark matter, as an isotropic lensing signal is excluded with 99.5% confidence. 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 an NFW profile. We adopt observationally motivated scaling relations between the lens luminosity and the velocity dispersion and the extent of the halo. The best fit NFW model yields a mass $M_{200}=(8.4\pm0.7\pm0.4)\times 10^{11} h^{-1} M_\odot$ and a scale radius $r_s=16.2^{+3.6}_{-2.9} h^{-1}$ kpc. This value for the scale radius is in excellent agreement with predictions from numerical simulations for a halo of this mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".