Velocity Dispersions of Massive Quiescent Galaxies from Weak Lensing and Spectroscopy*
Bibliographic record
Abstract
Abstract We use MMT spectroscopy and deep Subaru Hyper Suprime-Cam (HSC) imaging to compare the spectroscopic central stellar velocity dispersion of quiescent galaxies with the effective dispersion of the dark matter halo derived from the stacked lensing signal. The spectroscopic survey (the Smithsonian Hectospec Lensing Survey) provides a sample of 4585 quiescent galaxy lenses with measured line-of-sight central stellar velocity dispersion (σ SHELS) that is more than 85% complete for R < 20.6, D n 4000 > 1.5 and M ⋆ > 109.5 M ⊙. The median redshift of the sample of lenses is 0.32. We measure the stacked lensing signal from the HSC deep imaging. The central stellar velocity dispersion is directly proportional to the velocity dispersion derived from the lensing σ Lens, . The independent spectroscopic and weak lensing velocity dispersions probe different scales, ∼3 kpc and ≳100 kpc, respectively, and strongly indicate that the observable central stellar velocity dispersion for quiescent galaxies is a good proxy for the velocity dispersion of the dark matter halo. We thus demonstrate the power of combining high-quality imaging and spectroscopy to shed light on the connection between galaxies and their dark matter halos.
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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 | 0.003 | 0.001 |
| 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".