Stellar mass versus velocity dispersion as tracers of the lensing signal around bulge-dominated galaxies
Bibliographic record
Abstract
We present the results of a weak gravitational lensing analysis to determine whether the stellar mass or else the velocity dispersion is more closely related to the amplitude of the lensing signal around galaxies, hence to the projected distribution of dark matter. The lensing signal on smaller scales than the virial radius corresponds most closely to the lensing velocity dispersion in the case of a singular isothermal profile, but is also sensitive on larger scales to the clustering of the haloes. We have selected over 4000 lens galaxies at a redshift z < 0.2 with concentrated (or bulge-dominated) surface brightness profiles from the ~300 square degree overlap between the Red-sequence Cluster Survey 2 (RCS2) and the data release 7 (DR7) of the Sloan Digital Sky Survey (SDSS). We consider both the spectroscopic velocity dispersion and a model velocity dispersion (a combination of the stellar mass, the size, and the Sérsic index of a galaxy). Comparing the model and spectroscopic velocity dispersion we find that they correlate well for galaxies with concentrated brightness profiles. We find that the stellar mass and the spectroscopic velocity dispersion trace the amplitude of the lensing signal on small scales equally well. The model velocity dispersion, however, does significantly worse. A possible explanation is that the halo properties that determine the small-scale lensing signal – mainly the total mass – also depend on the structural parameters of galaxies, such as the effective radius and Sérsic index, but we lack data for a definitive conclusion.
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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 | 0.000 | 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) | 0.001 | 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".