MAGAZ3NE: High Stellar Velocity Dispersions for Ultramassive Quiescent Galaxies at z ≳ 3*
Bibliographic record
Abstract
Abstract In this work, we publish stellar velocity dispersions, sizes, and dynamical masses for eight ultramassive galaxies (UMGs; log ( M * / M ⊙ ) > 11), z ≳ 3) from the Massive Ancient Galaxies At z > 3 NEar-infrared (MAGAZ3NE) Survey, more than doubling the number of such galaxies with velocity dispersion measurements at this epoch. Using the deep Keck/MOSFIRE and Keck/NIRES spectroscopy of these objects in the H and K bandpasses, we obtain large velocity dispersions of ∼400 km s −1 for most of the objects, which are some of the highest stellar velocity dispersions measured and ∼40% larger than those measured for galaxies of similar mass at z ∼ 1.7. The sizes of these objects are also smaller by a factor of 1.5–3 compared to this same z ∼ 1.7 sample. We combine these large velocity dispersions and small sizes to obtain dynamical masses. The dynamical masses are similar to the stellar masses of these galaxies, consistent with a Chabrier initial mass function (IMF). Considered alongside previous studies of massive quiescent galaxies across 0.2 < z < 4.0, there is evidence for an evolution in the relation between the dynamical mass–stellar mass ratio and velocity dispersion as a function of redshift. This implies an IMF with fewer low-mass stars (e.g., Chabrier IMF) for massive quiescent galaxies at higher redshifts in conflict with the bottom-heavy IMF (e.g., Salpeter IMF) found in their likely z ∼ 0 descendants, though a number of alternative explanations such as a different dynamical structure or significant rotation are not ruled out. Similar to data at lower redshifts, we see evidence for an increase of IMF normalization with velocity dispersion, though the z ≳ 3 trend is steeper than that for z ∼ 0.2 early-type galaxies and offset to lower dynamical-to-stellar mass ratios.
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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 | 0.001 | 0.001 |
| 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) | 0.013 | 0.008 |
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".