MOSEL Survey: Tracking the Growth of Massive Galaxies at 2 < z < 4 Using Kinematics and the IllustrisTNG Simulation
Bibliographic record
Abstract
Abstract We use K-band spectroscopic data from the Multi-Object Spectroscopic Emission Line survey to analyze the kinematic properties of galaxies at z > 3. Our sample consists of 34 galaxies at 3.0 < z spec < 3.8 between 9.0 < <?CDATA $\mathrm{log}({M}_{* }/{M}_{\odot })$?> < 11.0. We find that galaxies with <?CDATA $\mathrm{log}({M}_{* }/{M}_{\odot })$?> > 10.2 at z > 3 have 56 ± 21 km s−1 lower integrated velocity dispersion compared to galaxies at z ≃ 2 of similar stellar mass. Massive galaxies at z > 3 have either a flat or declining star formation history (SFH), whereas similar stellar mass galaxies at z ∼ 2.0 exhibit a slight peak in the past 500 Myr. Comparing with the IllustrisTNG cosmological simulation, we find that (i) the dynamical mass of massive galaxies in simulations ( <?CDATA $\mathrm{log}({M}_{* }/{M}_{\odot })$?> > 10.0) increases by ∼0.1 dex at a fixed stellar mass between z = 2.0–3.0, and (ii) dynamical mass growth is coupled with a rapid rise in the ex situ stellar mass fraction (stars accreted from other galaxies) for massive galaxies at z < 3.5. We speculate that the rising contribution of ex situ stellar mass to the total stellar mass growth of massive galaxies is driving the higher integrated velocity dispersion and rising SFHs of massive galaxies at z ∼ 2.0 compared to galaxies of similar stellar masses at z > 3.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".