Weak-Lensing Study of Low-Mass Galaxy Groups: Implications for Ω[TINF][ITAL]m[/ITAL][/TINF]
Bibliographic record
Abstract
We report on the first measurement of the average mass and mass-to-light ratio of galaxy groups by analyzing the weak-lensing signal induced by these systems. The groups, which have velocity dispersions of 50-400 km s -1 , have been selected from the Canadian Network for Observational Cosmology Field Galaxy Redshift Survey (CNOC2). This survey allows the identification of a large number of groups with redshifts ranging from z = 0.12 to 0.55, ideal for a weak-lensing analysis of their mass distribution. For our analysis we use a sample of 50 groups that are selected on the basis of a careful dynamical analysis of group candidates. We detect a signal at the 99% confidence limit. The best-fit singular isothermal sphere model yields an Einstein radius r E = 0 72 ± 0 29. This corresponds to a velocity dispersion of ⟨σ 2 ⟩ 1/2 = 274 km s -1 (using photometric redshift distributions for the source galaxies), which is in good agreement with the dynamical estimate. Under the assumption that the light traces the mass, we find an average mass-to-light ratio of 191 ± 83 h M ☉ / L B ☉ in the rest-frame B band. Unlike dynamical estimates, this result is insensitive to problems associated with determining group membership. After correction of the observed mass-to-light ratio for luminosity evolution to z = 0, we find 254 ± 110 h M ☉ / L B ☉ , lower than what is found for rich clusters. We use the observed mass-to-light ratio to estimate the matter density of the universe, for which we find Ω m = 0.19 ± 0.10 (Ω Λ = 0), in good agreement with other recent estimates. For a closed universe (Ω m + Ω Λ = 1), we obtain Ω m = 0.13 ± 0.07.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".