Constraints on the shapes of galaxy dark matter haloes from weak gravitational lensing
Bibliographic record
Abstract
We study the shapes of galaxy dark matter haloes by measuring the anisotropy of the weak gravitational lensing signal around galaxies in the second Red-sequence Cluster Survey (RCS2). We determine the average shear anisotropy within the virial radius for three lens samples: the “all” sample, which contains all galaxies with 19 < mr′ < 21.5, and the “red” and “blue” samples, whose lensing signals are dominated by massive low-redshift early-type and late-type galaxies, respectively. To study the environmental dependence of the lensing signal, we separate each lens sample into an isolated and clustered part and analyse them separately. We address the impact of several complications on the halo ellipticity measurement, including PSF residual systematics in the shape catalogues, multiple deflections, and the clustering of lenses. We estimate that the impact of these is small for our lens selections. Furthermore, we measure the azimuthal dependence of the distribution of physically associated galaxies around the lens samples. We find that these satellites preferentially reside near the major axis of the lenses, and constrain the angle between the major axis of the lens and the average location of the satellites to ⟨θ⟩ = 43.7° ± 0.3° for the “all” lenses, ⟨θ⟩ = 41.7° ± 0.5° for the “red” lenses and ⟨θ⟩ = 42.0° ± 1.4° for the “blue” lenses. We do not detect a significant shear anisotropy for the average “red” and “blue” lenses, although for the most elliptical “red” and “blue” galaxies it is marginally positive and negative, respectively. For the “all” sample, we find that the anisotropy of the galaxy-mass cross-correlation function ⟨f − f45⟩ = 0.23 ± 0.12, providing weak support for the view that the average galaxy is embedded in, and preferentially aligned with, a triaxial dark matter halo. Assuming an elliptical Navarro-Frenk-White profile, we find that the ratio of the dark matter halo ellipticity and the galaxy ellipticity fh = eh/eg = 1.50-1.01+1.03, which for a mean lens ellipticity of 0.25 corresponds to a projected halo ellipticity of eh = 0.38-0.25+0.26 if the halo and the lens are perfectly aligned. For isolated galaxies of the “all” sample, the average shear anisotropy increases to ⟨f-f45⟩ = 0.51-0.25+0.26 and fh = 4.73-2.05+2.17, whilst for clustered galaxies the signal is consistent with zero. These constraints provide lower limits on the average dark matter halo ellipticity, as scatter in the relative position angle between the galaxies and the dark matter haloes is expected to reduce the shear anisotropy by a factor ~2.
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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 | 0.001 |
| 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".