Weak-lensing analysis of galaxy pairs using CS82 data
Bibliographic record
Abstract
Here we analyze a sample of close galaxy pairs (relative projected separation < 25 h -1 kpc and relative radial velocities < 350 km s -1 ) using a weak-lensing analysis based on the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82). We determine halo masses for the total sample of pairs as well as for interacting, red, and higher-luminosity pair subsamples with ∼3σ confidence. The derived lensing signal for the total sample can be fitted either by a Singular Isothermal Sphere (SIS) with σ V = 223 ± 24 km s -1 or a Navarro-Frenk-White (NFW) profile with R 200 = 0.30 ± 0.03 h -1 Mpc. The pair total masses and total r band luminosities imply an average mass-to-light ratio of ∼200 h M ⊙ /L ⊙ . On the other hand, red pairs which include a larger fraction of elliptical galaxies, show a larger mass-to-light ratio of ∼345 h M ⊙ /L ⊙ . Derived lensing masses were compared to a proxy of the dynamical mass, obtaining a good correlation. However, there is a large discrepancy between lensing masses and the dynamical mass estimates, which could be accounted for by astrophysical processes such as dynamical friction, by the inclusion of unbound pairs, and by significant deviations of the density distribution from SIS and NFW profiles in the inner regions. We also compared lensing masses with group mass estimates, finding very good agreement with the sample of groups with two members. Red and blue pairs show large differences between group and lensing masses, which is likely due to the single mass-to-light ratio adopted to compute the group masses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".