REVISED MASS-TO-LIGHT RATIOS FOR NEARBY GALAXY GROUPS AND CLUSTERS
Bibliographic record
Abstract
We present a detailed investigation of the cluster stellar mass-to-light ( M */ L ) ratio and cumulative stellar masses, derived on a galaxy-by-galaxy basis, for 12 massive ( M 500 ∼ 10 14 –10 15 M ☉ ), nearby clusters with available optical imaging data from the Sloan Digital Sky Survey Data Release 10 and X-ray data from the Chandra X-ray Observatory . Our method involves a statistical cluster membership using both photometric and spectroscopic redshifts when available to maximize completeness while minimizing contamination effects. We show that different methods of estimating the stellar mass-to-light ratio from observed photometry result in systematic discrepancies in the total stellar masses and average mass-to-light ratios of cluster galaxies. Nonetheless, all conversion methodologies point to a lack of correlation between M */ L i and total cluster mass, even though low-mass groups contain relatively more blue galaxies. We also find no statistically significant correlation between M */ L i and the fraction of blue galaxies ( g − i < 0.85). For the mass range covered by our sample, the assumption of a Chabrier initial mass function (IMF) yields an integrated M */ L i ≃ 1.7 ± 0.2 M ☉ / L i , ☉ , a lower value than used in most similar studies, though consistent with the study of low-mass galaxy groups by Leauthaud et al. A light (diet) Salpeter IMF would imply a ∼60% increase in M */ L i .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".